diff --git a/ax/core/objective.py b/ax/core/objective.py
index 653c0f9afc1..151e331d8ea 100644
--- a/ax/core/objective.py
+++ b/ax/core/objective.py
@@ -13,6 +13,7 @@
 from typing import Any, Iterable, List, Optional, Tuple
 
 from ax.core.metric import Metric
+from ax.exceptions.core import UserInputError
 from ax.utils.common.base import SortableBase
 from ax.utils.common.logger import get_logger
 from ax.utils.common.typeutils import not_none
@@ -34,36 +35,27 @@ def __init__(self, metric: Metric, minimize: Optional[bool] = None) -> None:
             metric: The metric to be optimized.
             minimize: If True, minimize metric. If None, will be set based on the
                 `lower_is_better` property of the metric (if that is not specified,
-                will raise a DeprecationWarning).
+                will raise a `UserInputError`).
 
         """
         lower_is_better = metric.lower_is_better
         if minimize is None:
             if lower_is_better is None:
-                warnings.warn(
-                    f"Defaulting to `minimize=False` for metric {metric.name} not "
-                    + "specifying `lower_is_better` property. This is a wild guess. "
-                    + "Specify either `lower_is_better` on the metric, or specify "
-                    + "`minimize` explicitly. This will become an error in the future.",
-                    DeprecationWarning,
+                raise UserInputError(
+                    f"Metric {metric.name} does not specify `lower_is_better` "
+                    "and `minimize` is not specified. At least one of these "
+                    "must be specified."
                 )
-                minimize = False
             else:
                 minimize = lower_is_better
-        if lower_is_better is not None:
-            if lower_is_better and not minimize:
-                warnings.warn(
-                    f"Attempting to maximize metric {metric.name} with property "
-                    "`lower_is_better=True`."
-                )
-            elif not lower_is_better and minimize:
-                warnings.warn(
-                    f"Attempting to minimize metric {metric.name} with property "
-                    "`lower_is_better=False`."
-                )
-        self._metric = metric
-        # pyre-fixme[4]: Attribute must be annotated.
-        self.minimize = not_none(minimize)
+        elif lower_is_better is not None and lower_is_better != minimize:
+            raise UserInputError(
+                f"Metric {metric.name} specifies {lower_is_better=}, "
+                "which doesn't match the specified optimization direction "
+                f"{minimize=}."
+            )
+        self._metric: Metric = metric
+        self.minimize: bool = not_none(minimize)
 
     @property
     def metric(self) -> Metric:
@@ -130,18 +122,17 @@ def __init__(
                     "as input to `MultiObjective` constructor."
                 )
             metrics = extra_kwargs["metrics"]
-            minimize = extra_kwargs.get("minimize", False)
+            minimize = extra_kwargs.get("minimize", None)
             warnings.warn(
                 "Passing `metrics` and `minimize` as input to the `MultiObjective` "
                 "constructor will soon be deprecated. Instead, pass a list of "
                 "`objectives`. This will become an error in the future.",
                 DeprecationWarning,
+                stacklevel=2,
             )
             objectives = []
             for metric in metrics:
-                lower_is_better = metric.lower_is_better or False
-                _minimize = not lower_is_better if minimize else lower_is_better
-                objectives.append(Objective(metric=metric, minimize=_minimize))
+                objectives.append(Objective(metric=metric, minimize=minimize))
 
         # pyre-fixme[4]: Attribute must be annotated.
         self._objectives = not_none(objectives)
diff --git a/ax/core/tests/test_objective.py b/ax/core/tests/test_objective.py
index 3c6e7a3059d..39bdc0d9fc2 100644
--- a/ax/core/tests/test_objective.py
+++ b/ax/core/tests/test_objective.py
@@ -6,10 +6,9 @@
 
 # pyre-strict
 
-import warnings
-
 from ax.core.metric import Metric
 from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
+from ax.exceptions.core import UserInputError
 from ax.utils.common.testutils import TestCase
 
 
@@ -21,7 +20,7 @@ def setUp(self) -> None:
             "m3": Metric(name="m3", lower_is_better=False),
         }
         self.objectives = {
-            "o1": Objective(metric=self.metrics["m1"]),
+            "o1": Objective(metric=self.metrics["m1"], minimize=True),
             "o2": Objective(metric=self.metrics["m2"], minimize=True),
             "o3": Objective(metric=self.metrics["m3"], minimize=False),
         }
@@ -38,6 +37,12 @@ def setUp(self) -> None:
         )
 
     def test_Init(self) -> None:
+        with self.assertRaisesRegex(UserInputError, "does not specify"):
+            Objective(metric=self.metrics["m1"]),
+        with self.assertRaisesRegex(
+            UserInputError, "doesn't match the specified optimization direction"
+        ):
+            Objective(metric=self.metrics["m2"], minimize=False)
         with self.assertRaises(ValueError):
             ScalarizedObjective(
                 metrics=[self.metrics["m1"], self.metrics["m2"]], weights=[1.0]
@@ -52,20 +57,6 @@ def test_Init(self) -> None:
                 metrics=[self.metrics["m1"], self.metrics["m2"]],
                 minimize=False,
             )
-        warnings.resetwarnings()
-        warnings.simplefilter("always", append=True)
-        with warnings.catch_warnings(record=True) as ws:
-            Objective(metric=self.metrics["m1"])
-            self.assertTrue(any(issubclass(w.category, DeprecationWarning) for w in ws))
-            self.assertTrue(
-                any("Defaulting to `minimize=False`" in str(w.message) for w in ws)
-            )
-        with warnings.catch_warnings(record=True) as ws:
-            Objective(Metric(name="m4", lower_is_better=True), minimize=False)
-            self.assertTrue(any("Attempting to maximize" in str(w.message) for w in ws))
-        with warnings.catch_warnings(record=True) as ws:
-            Objective(Metric(name="m4", lower_is_better=False), minimize=True)
-            self.assertTrue(any("Attempting to minimize" in str(w.message) for w in ws))
         self.assertEqual(
             self.objective.get_unconstrainable_metrics(), [self.metrics["m1"]]
         )
@@ -77,7 +68,7 @@ def test_MultiObjective(self) -> None:
 
         self.assertEqual(self.multi_objective.metrics, list(self.metrics.values()))
         minimizes = [obj.minimize for obj in self.multi_objective.objectives]
-        self.assertEqual(minimizes, [False, True, False])
+        self.assertEqual(minimizes, [True, True, False])
         weights = [mw[1] for mw in self.multi_objective.objective_weights]
         self.assertEqual(weights, [1.0, 1.0, 1.0])
         self.assertEqual(self.multi_objective.clone(), self.multi_objective)
@@ -85,7 +76,7 @@ def test_MultiObjective(self) -> None:
             str(self.multi_objective),
             (
                 "MultiObjective(objectives="
-                '[Objective(metric_name="m1", minimize=False), '
+                '[Objective(metric_name="m1", minimize=True), '
                 'Objective(metric_name="m2", minimize=True), '
                 'Objective(metric_name="m3", minimize=False)])'
             ),
@@ -96,19 +87,26 @@ def test_MultiObjective(self) -> None:
         )
 
     def test_MultiObjectiveBackwardsCompatibility(self) -> None:
-        multi_objective = MultiObjective(
-            metrics=[self.metrics["m1"], self.metrics["m2"], self.metrics["m3"]]
-        )
+        metrics = [
+            Metric(name="m1", lower_is_better=False),
+            self.metrics["m2"],
+            self.metrics["m3"],
+        ]
+        multi_objective = MultiObjective(metrics=metrics)
         minimizes = [obj.minimize for obj in multi_objective.objectives]
-        self.assertEqual(multi_objective.metrics, list(self.metrics.values()))
+        self.assertEqual(multi_objective.metrics, metrics)
         self.assertEqual(minimizes, [False, True, False])
 
         multi_objective_min = MultiObjective(
-            metrics=[self.metrics["m1"], self.metrics["m2"], self.metrics["m3"]],
+            metrics=[
+                Metric(name="m1"),
+                Metric(name="m2"),
+                Metric(name="m3", lower_is_better=True),
+            ],
             minimize=True,
         )
         minimizes = [obj.minimize for obj in multi_objective_min.objectives]
-        self.assertEqual(minimizes, [True, False, True])
+        self.assertEqual(minimizes, [True, True, True])
 
     def test_ScalarizedObjective(self) -> None:
         with self.assertRaises(NotImplementedError):
diff --git a/ax/core/tests/test_optimization_config.py b/ax/core/tests/test_optimization_config.py
index 30739d8c512..f3a484c279a 100644
--- a/ax/core/tests/test_optimization_config.py
+++ b/ax/core/tests/test_optimization_config.py
@@ -277,7 +277,7 @@ def setUp(self) -> None:
             "o2": Objective(metric=self.metrics["m2"], minimize=False),
             "o3": Objective(metric=self.metrics["m3"], minimize=False),
         }
-        self.objective = Objective(metric=self.metrics["m1"], minimize=False)
+        self.objective = Objective(metric=self.metrics["m1"], minimize=True)
         self.multi_objective = MultiObjective(
             objectives=[self.objectives["o1"], self.objectives["o2"]]
         )
diff --git a/ax/core/tests/test_utils.py b/ax/core/tests/test_utils.py
index a5c5f5ab129..cede5005733 100644
--- a/ax/core/tests/test_utils.py
+++ b/ax/core/tests/test_utils.py
@@ -158,7 +158,7 @@ def setUp(self) -> None:
         self.data = Data(df=self.df)
 
         self.optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric(name="a")),
+            objective=Objective(metric=Metric(name="a"), minimize=False),
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric(name="b"),
diff --git a/ax/modelbridge/tests/test_base_modelbridge.py b/ax/modelbridge/tests/test_base_modelbridge.py
index cb3ce7e9e53..f7ad8e5c233 100644
--- a/ax/modelbridge/tests/test_base_modelbridge.py
+++ b/ax/modelbridge/tests/test_base_modelbridge.py
@@ -156,7 +156,7 @@ def test_ModelBridge(
                 observation_features=[get_observation1trans().features], weights=[2]
             ),
         )
-        oc = OptimizationConfig(objective=Objective(metric=Metric(name="test_metric")))
+        oc = get_optimization_config_no_constraints()
         modelbridge._set_kwargs_to_save(
             model_key="TestModel", model_kwargs={}, bridge_kwargs={}
         )
@@ -322,7 +322,7 @@ def warn_and_return_mock_obs(
             fit_tracking_metrics=False,
         )
         new_oc = OptimizationConfig(
-            objective=Objective(metric=Metric(name="test_metric2"))
+            objective=Objective(metric=Metric(name="test_metric2"), minimize=False),
         )
         with self.assertRaisesRegex(UnsupportedError, "fit_tracking_metrics"):
             modelbridge.gen(n=1, optimization_config=new_oc)
diff --git a/ax/modelbridge/tests/test_cross_validation.py b/ax/modelbridge/tests/test_cross_validation.py
index 7ede3cd7e89..02d5e650e05 100644
--- a/ax/modelbridge/tests/test_cross_validation.py
+++ b/ax/modelbridge/tests/test_cross_validation.py
@@ -344,7 +344,7 @@ def test_HasGoodOptConfigModelFit(self) -> None:
 
         # Test single objective
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("a"))
+            objective=Objective(metric=Metric("a"), minimize=True)
         )
         has_good_fit = has_good_opt_config_model_fit(
             optimization_config=optimization_config,
@@ -354,7 +354,12 @@ def test_HasGoodOptConfigModelFit(self) -> None:
 
         # Test multi objective
         optimization_config = MultiObjectiveOptimizationConfig(
-            objective=MultiObjective(metrics=[Metric("a"), Metric("b")])
+            objective=MultiObjective(
+                objectives=[
+                    Objective(Metric("a"), minimize=False),
+                    Objective(Metric("b"), minimize=False),
+                ]
+            )
         )
         has_good_fit = has_good_opt_config_model_fit(
             optimization_config=optimization_config,
@@ -364,7 +369,7 @@ def test_HasGoodOptConfigModelFit(self) -> None:
 
         # Test constraints
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("a")),
+            objective=Objective(metric=Metric("a"), minimize=False),
             outcome_constraints=[
                 OutcomeConstraint(metric=Metric("b"), op=ComparisonOp.GEQ, bound=0.1)
             ],
diff --git a/ax/modelbridge/tests/test_torch_modelbridge.py b/ax/modelbridge/tests/test_torch_modelbridge.py
index a0466356f66..2062fac283f 100644
--- a/ax/modelbridge/tests/test_torch_modelbridge.py
+++ b/ax/modelbridge/tests/test_torch_modelbridge.py
@@ -45,6 +45,7 @@
     get_branin_experiment,
     get_branin_search_space,
     get_experiment_with_observations,
+    get_optimization_config_no_constraints,
     get_search_space_for_range_value,
 )
 from ax.utils.testing.mock import fast_botorch_optimize
@@ -363,9 +364,7 @@ def test_evaluate_acquisition_function(self, _, mock_torch_model: Mock) -> None:
                 observation_features=[
                     ObservationFeatures(parameters={"x": 1.0, "y": 2.0})
                 ],
-                optimization_config=OptimizationConfig(
-                    objective=Objective(metric=Metric(name="test_metric"))
-                ),
+                optimization_config=get_optimization_config_no_constraints(),
             )
 
         self.assertEqual(acqf_vals, [5.0])
@@ -392,9 +391,7 @@ def test_evaluate_acquisition_function(self, _, mock_torch_model: Mock) -> None:
                     ObservationFeatures(parameters={"x": 1.0, "y": 2.0}),
                     ObservationFeatures(parameters={"x": 1.0, "y": 2.0}),
                 ],
-                optimization_config=OptimizationConfig(
-                    objective=Objective(metric=Metric(name="test_metric"))
-                ),
+                optimization_config=get_optimization_config_no_constraints(),
             )
         t.transform_observation_features.assert_any_call(
             [ObservationFeatures(parameters={"x": 1.0, "y": 2.0})],
@@ -418,9 +415,7 @@ def test_evaluate_acquisition_function(self, _, mock_torch_model: Mock) -> None:
                         ObservationFeatures(parameters={"x": 1.0, "y": 2.0}),
                     ]
                 ],
-                optimization_config=OptimizationConfig(
-                    objective=Objective(metric=Metric(name="test_metric"))
-                ),
+                optimization_config=get_optimization_config_no_constraints(),
             )
         t.transform_observation_features.assert_any_call(
             [
diff --git a/ax/modelbridge/tests/test_utils.py b/ax/modelbridge/tests/test_utils.py
index c90db16ba4d..3f09ae770b0 100644
--- a/ax/modelbridge/tests/test_utils.py
+++ b/ax/modelbridge/tests/test_utils.py
@@ -117,7 +117,9 @@ def test_extract_outcome_constraints(self) -> None:
     def test_extract_objective_thresholds(self) -> None:
         outcomes = ["m1", "m2", "m3", "m4"]
         objective = MultiObjective(
-            objectives=[Objective(metric=Metric(name)) for name in outcomes[:3]]
+            objectives=[
+                Objective(metric=Metric(name), minimize=False) for name in outcomes[:3]
+            ]
         )
         objective_thresholds = [
             ObjectiveThreshold(
@@ -159,7 +161,7 @@ def test_extract_objective_thresholds(self) -> None:
         self.assertTrue(np.isnan(obj_t[-2:]).all())
 
         # Fails if a threshold does not have a corresponding metric.
-        objective2 = Objective(Metric("m1"))
+        objective2 = Objective(Metric("m1"), minimize=False)
         with self.assertRaisesRegex(ValueError, "corresponding metrics"):
             extract_objective_thresholds(
                 objective_thresholds=objective_thresholds,
diff --git a/ax/modelbridge/transforms/tests/test_derelativize_transform.py b/ax/modelbridge/transforms/tests/test_derelativize_transform.py
index 924ee4d2f05..8a436709d3f 100644
--- a/ax/modelbridge/transforms/tests/test_derelativize_transform.py
+++ b/ax/modelbridge/transforms/tests/test_derelativize_transform.py
@@ -102,7 +102,7 @@ def test_DerelativizeTransform(
         )
 
         # Test with no relative constraints
-        objective = Objective(Metric("c"))
+        objective = Objective(Metric("c"), minimize=True)
         oc = OptimizationConfig(
             objective=objective,
             outcome_constraints=[
@@ -300,7 +300,7 @@ def test_Errors(self) -> None:
             observations=[],
         )
         oc = OptimizationConfig(
-            objective=Objective(Metric("c")),
+            objective=Objective(Metric("c"), minimize=False),
             outcome_constraints=[
                 OutcomeConstraint(Metric("a"), ComparisonOp.LEQ, bound=2, relative=True)
             ],
diff --git a/ax/modelbridge/transforms/tests/test_winsorize_transform.py b/ax/modelbridge/transforms/tests/test_winsorize_transform.py
index 7489bfe707b..ee91a9e16b0 100644
--- a/ax/modelbridge/transforms/tests/test_winsorize_transform.py
+++ b/ax/modelbridge/transforms/tests/test_winsorize_transform.py
@@ -581,7 +581,7 @@ def test_relative_constraints(
                 RangeParameter("y", ParameterType.FLOAT, 0, 20),
             ]
         )
-        objective = Objective(Metric("c"))
+        objective = Objective(Metric("c"), minimize=False)
 
         # Test with relative constraint, in-design status quo
         oc = OptimizationConfig(
diff --git a/ax/service/tests/scheduler_test_utils.py b/ax/service/tests/scheduler_test_utils.py
index 29fc52be056..c8a4f4b8887 100644
--- a/ax/service/tests/scheduler_test_utils.py
+++ b/ax/service/tests/scheduler_test_utils.py
@@ -321,7 +321,7 @@ def setUp(self) -> None:
         self.branin_experiment_no_impl_runner_or_metrics = Experiment(
             search_space=get_branin_search_space(),
             optimization_config=OptimizationConfig(
-                objective=Objective(metric=Metric(name="branin"))
+                objective=Objective(metric=Metric(name="branin"), minimize=False)
             ),
             name="branin_experiment_no_impl_runner_or_metrics",
         )
diff --git a/ax/service/tests/test_report_utils.py b/ax/service/tests/test_report_utils.py
index 0040f76f9d1..dd97206e8c1 100644
--- a/ax/service/tests/test_report_utils.py
+++ b/ax/service/tests/test_report_utils.py
@@ -560,11 +560,11 @@ def test_get_metric_name_pairs(self) -> None:
         exp._optimization_config = MultiObjectiveOptimizationConfig(
             objective=MultiObjective(
                 objectives=[
-                    Objective(metric=Metric("m0")),
-                    Objective(metric=Metric("m1")),
-                    Objective(metric=Metric("m2")),
-                    Objective(metric=Metric("m3")),
-                    Objective(metric=Metric("m4")),
+                    Objective(metric=Metric("m0"), minimize=False),
+                    Objective(metric=Metric("m1"), minimize=False),
+                    Objective(metric=Metric("m2"), minimize=False),
+                    Objective(metric=Metric("m3"), minimize=False),
+                    Objective(metric=Metric("m4"), minimize=False),
                 ]
             )
         )
@@ -1052,9 +1052,9 @@ def test_compare_to_baseline_moo(self) -> None:
             optimization_config = MultiObjectiveOptimizationConfig(
                 objective=MultiObjective(
                     objectives=[
-                        Objective(metric=Metric("m0")),
+                        Objective(metric=Metric("m0"), minimize=False),
                         Objective(metric=Metric("m1"), minimize=True),
-                        Objective(metric=Metric("m3")),
+                        Objective(metric=Metric("m3"), minimize=False),
                     ]
                 )
             )
diff --git a/ax/service/utils/report_utils.py b/ax/service/utils/report_utils.py
index f917b0cee59..d1ef02f1e38 100644
--- a/ax/service/utils/report_utils.py
+++ b/ax/service/utils/report_utils.py
@@ -25,7 +25,6 @@
 )
 
 import gpytorch
-
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
@@ -140,7 +139,7 @@ def _get_objective_trace_plot(
         plot_objective_value_vs_trial_index(
             exp_df=exp_df,
             metric_colname=metric_name,
-            minimize=(
+            minimize=not_none(
                 optimization_config.objective.minimize
                 if optimization_config.objective.metric.name == metric_name
                 else experiment.metrics[metric_name].lower_is_better
diff --git a/ax/storage/json_store/decoder.py b/ax/storage/json_store/decoder.py
index 664eb4784ba..b35a467cc3c 100644
--- a/ax/storage/json_store/decoder.py
+++ b/ax/storage/json_store/decoder.py
@@ -12,7 +12,7 @@
 from inspect import isclass
 from io import StringIO
 from logging import Logger
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import pandas as pd
@@ -25,6 +25,7 @@
 from ax.core.experiment import Experiment
 from ax.core.generator_run import GeneratorRun
 from ax.core.multi_type_experiment import MultiTypeExperiment
+from ax.core.objective import Objective
 from ax.core.parameter import Parameter
 from ax.core.parameter_constraint import (
     OrderConstraint,
@@ -49,16 +50,20 @@
     tensor_from_json,
     trial_from_json,
 )
-
 from ax.storage.json_store.registry import (
     CORE_CLASS_DECODER_REGISTRY,
     CORE_DECODER_REGISTRY,
 )
 from ax.utils.common.logger import get_logger
-from ax.utils.common.serialization import SerializationMixin
+from ax.utils.common.serialization import (
+    SerializationMixin,
+    TClassDecoderRegistry,
+    TDecoderRegistry,
+)
 from ax.utils.common.typeutils import checked_cast, not_none
 from ax.utils.common.typeutils_torch import torch_type_from_str
 
+
 logger: Logger = get_logger(__name__)
 
 
@@ -66,13 +71,8 @@
 def object_from_json(
     # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     object_json: Any,
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Any:
     """Recursively load objects from a JSON-serializable dictionary."""
     if type(object_json) in (str, int, float, bool, type(None)) or isinstance(
@@ -219,6 +219,12 @@ def object_from_json(
                 decoder_registry=decoder_registry,
                 class_decoder_registry=class_decoder_registry,
             )
+        elif _class == Objective:
+            return objective_from_json(
+                object_json=object_json,
+                decoder_registry=decoder_registry,
+                class_decoder_registry=class_decoder_registry,
+            )
         elif _class == TorchvisionBenchmarkProblem:
             return TorchvisionBenchmarkProblem.from_dataset_name(
                 name=object_json["name"],
@@ -289,13 +295,8 @@ def ax_class_from_json_dict(
     #  `typing.Type` to avoid runtime subscripting errors.
     _class: Type,
     object_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Any:
     """Reinstantiates an Ax class registered in `DECODER_REGISTRY` from a JSON
     dict.
@@ -314,13 +315,8 @@ def ax_class_from_json_dict(
 
 def generator_run_from_json(
     object_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> GeneratorRun:
     """Load Ax GeneratorRun from JSON."""
     time_created_json = object_json.pop("time_created")
@@ -359,13 +355,8 @@ def trial_transition_criteria_from_json(
     #  avoid runtime subscripting errors.
     class_: Type,
     transition_criteria_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Optional[TransitionCriterion]:
     """Load Ax transition criteria that depend on Trials from JSON.
 
@@ -389,13 +380,8 @@ def trial_transition_criteria_from_json(
 
 def search_space_from_json(
     search_space_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> SearchSpace:
     """Load a SearchSpace from JSON.
 
@@ -422,13 +408,8 @@ def search_space_from_json(
 def parameter_constraints_from_json(
     parameter_constraint_json: List[Dict[str, Any]],
     parameters: List[Parameter],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> List[ParameterConstraint]:
     """Load ParameterConstraints from JSON.
 
@@ -476,13 +457,8 @@ def parameter_constraints_from_json(
 def trials_from_json(
     experiment: Experiment,
     trials_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Dict[int, BaseTrial]:
     """Load Ax Trials from JSON."""
     loaded_trials = {}
@@ -507,13 +483,8 @@ def trials_from_json(
 
 def data_from_json(
     data_by_trial_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Dict[int, "OrderedDict[int, Data]"]:
     """Load Ax Data from JSON."""
     data_by_trial = object_from_json(
@@ -531,13 +502,8 @@ def data_from_json(
 
 def multi_type_experiment_from_json(
     object_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> MultiTypeExperiment:
     """Load AE MultiTypeExperiment from JSON."""
     experiment_info = _get_experiment_info(object_json)
@@ -585,13 +551,8 @@ def multi_type_experiment_from_json(
 
 def experiment_from_json(
     object_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Experiment:
     """Load Ax Experiment from JSON."""
     experiment_info = _get_experiment_info(object_json)
@@ -630,13 +591,8 @@ def _get_experiment_info(object_json: Dict[str, Any]) -> Dict[str, Any]:
 def _load_experiment_info(
     exp: Experiment,
     exp_info: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> None:
     """Loads `Experiment` object with basic information."""
     exp._time_created = object_from_json(
@@ -690,13 +646,8 @@ def _convert_generation_step_keys_for_backwards_compatibility(
 
 def generation_node_from_json(
     generation_node_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> GenerationNode:
     """Load GenerationNode object from JSON."""
     return GenerationNode(
@@ -724,13 +675,8 @@ def generation_node_from_json(
 
 def generation_step_from_json(
     generation_step_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> GenerationStep:
     """Load generation step from JSON."""
     generation_step_json = _convert_generation_step_keys_for_backwards_compatibility(
@@ -790,13 +736,8 @@ def generation_step_from_json(
 
 def model_spec_from_json(
     model_spec_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> ModelSpec:
     """Load ModelSpec from JSON."""
     kwargs = model_spec_json.pop("model_kwargs", None)
@@ -834,14 +775,9 @@ def model_spec_from_json(
 
 def generation_strategy_from_json(
     generation_strategy_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
     experiment: Optional[Experiment] = None,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> GenerationStrategy:
     """Load generation strategy from JSON."""
     nodes = (
@@ -890,13 +826,8 @@ def generation_strategy_from_json(
 
 def surrogate_from_list_surrogate_json(
     list_surrogate_json: Dict[str, Any],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Surrogate:
     logger.warning(
         "`ListSurrogate` has been deprecated. Reconstructing a `Surrogate` "
@@ -970,13 +901,8 @@ def surrogate_from_list_surrogate_json(
 
 def get_input_transform_json_components(
     input_transforms_json: Optional[Union[List[Dict[str, Any]], Dict[str, Any]]],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Tuple[Optional[List[Dict[str, Any]]], Optional[Dict[str, Any]]]:
     if input_transforms_json is None:
         return None, None
@@ -1003,13 +929,8 @@ def get_input_transform_json_components(
 
 def get_outcome_transform_json_components(
     outcome_transforms_json: Optional[List[Dict[str, Any]]],
-    # pyre-fixme[24]: Generic type `type` expects 1 type parameter, use
-    #  `typing.Type` to avoid runtime subscripting errors.
-    decoder_registry: Dict[str, Type] = CORE_DECODER_REGISTRY,
-    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
-    class_decoder_registry: Dict[
-        str, Callable[[Dict[str, Any]], Any]
-    ] = CORE_CLASS_DECODER_REGISTRY,
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
 ) -> Tuple[Optional[List[Dict[str, Any]]], Optional[Dict[str, Any]]]:
     if outcome_transforms_json is None:
         return None, None
@@ -1030,3 +951,39 @@ def get_outcome_transform_json_components(
         for outcome_transform_json in outcome_transforms_json
     }
     return outcome_transform_classes_json, outcome_transform_options_json
+
+
+def objective_from_json(
+    object_json: Dict[str, Any],
+    decoder_registry: TDecoderRegistry = CORE_DECODER_REGISTRY,
+    class_decoder_registry: TClassDecoderRegistry = CORE_CLASS_DECODER_REGISTRY,
+) -> Objective:
+    """Load an ``Objective`` from JSON in a backwards compatible way.
+
+    If both ``minimize`` and ``lower_is_better`` are specified but have conflicting
+    values, this will overwrite ``lower_is_better=minimize`` to resolve the conflict.
+
+    # TODO: Do we need to do this for scalarized objective as well?
+    """
+    input_args = {
+        k: object_from_json(
+            v,
+            decoder_registry=decoder_registry,
+            class_decoder_registry=class_decoder_registry,
+        )
+        for k, v in object_json.items()
+    }
+    metric = input_args.pop("metric")
+    minimize = input_args.pop("minimize")
+    if metric.lower_is_better is not None and metric.lower_is_better != minimize:
+        logger.warning(
+            f"Metric {metric.name} has {metric.lower_is_better=} but objective "
+            f"specifies {minimize=}. Overwriting ``lower_is_better`` to match "
+            f"the optimization direction {minimize=}."
+        )
+        metric.lower_is_better = minimize
+    return Objective(
+        metric=metric,
+        minimize=minimize,
+        **input_args,  # For future compatibility.
+    )
diff --git a/ax/storage/json_store/tests/test_json_store.py b/ax/storage/json_store/tests/test_json_store.py
index 8ca5b520b2d..a6b73277aa4 100644
--- a/ax/storage/json_store/tests/test_json_store.py
+++ b/ax/storage/json_store/tests/test_json_store.py
@@ -14,6 +14,7 @@
 import torch
 from ax.benchmark.metrics.jenatton import JenattonMetric
 from ax.core.metric import Metric
+from ax.core.objective import Objective
 from ax.core.runner import Runner
 from ax.exceptions.core import AxStorageWarning
 from ax.exceptions.storage import JSONDecodeError, JSONEncodeError
@@ -652,3 +653,17 @@ def test_BadStateDict(self) -> None:
             expected_json = botorch_component_to_dict(interval)
             del expected_json["state_dict"]["lower_bound"]
             botorch_component_from_json(interval.__class__, expected_json)
+
+    def test_objective_backwards_compatibility(self) -> None:
+        # Test that we can load an objective that has conflicting
+        # ``lower_is_better`` and ``minimize`` fields.
+        objective = get_objective(minimize=True)
+        objective.metric.lower_is_better = False  # for conflict!
+        objective_json = object_to_json(objective)
+        self.assertTrue(objective_json["minimize"])
+        self.assertFalse(objective_json["metric"]["lower_is_better"])
+        objective_loaded = object_from_json(objective_json)
+        self.assertIsInstance(objective_loaded, Objective)
+        self.assertNotEqual(objective, objective_loaded)
+        self.assertTrue(objective_loaded.minimize)
+        self.assertTrue(objective_loaded.metric.lower_is_better)
diff --git a/ax/storage/sqa_store/decoder.py b/ax/storage/sqa_store/decoder.py
index 66d703e274e..38ed9a950c4 100644
--- a/ax/storage/sqa_store/decoder.py
+++ b/ax/storage/sqa_store/decoder.py
@@ -1028,7 +1028,16 @@ def _objective_from_sqa(self, metric: Metric, metric_sqa: SQAMetric) -> Objectiv
                 f"The metric {metric.name} corresponding to regular objective does not "
                 "have weight attribute"
             )
-        return Objective(metric=metric, minimize=metric_sqa.minimize)
+        # Resolve any conflicts between ``lower_is_better`` and ``minimize``.
+        minimize = metric_sqa.minimize
+        if metric.lower_is_better is not None and metric.lower_is_better != minimize:
+            logger.warning(
+                f"Metric {metric.name} has {metric.lower_is_better=} but objective "
+                f"specifies {minimize=}. Overwriting ``lower_is_better`` to match "
+                f"the optimization direction {minimize=}."
+            )
+            metric.lower_is_better = minimize
+        return Objective(metric=metric, minimize=minimize)
 
     def _multi_objective_from_sqa(self, parent_metric_sqa: SQAMetric) -> Objective:
         try:
@@ -1054,9 +1063,9 @@ def _multi_objective_from_sqa(self, parent_metric_sqa: SQAMetric) -> Objective:
 
         # Extracting metric and weight for each child
         objectives = [
-            Objective(
+            self._objective_from_sqa(
                 metric=self._metric_from_sqa_util(parent_metric_sqa),
-                minimize=parent_metric_sqa.minimize,
+                metric_sqa=parent_metric_sqa,
             )
             for parent_metric_sqa in metrics_sqa_children
         ]
diff --git a/ax/storage/sqa_store/tests/test_sqa_store.py b/ax/storage/sqa_store/tests/test_sqa_store.py
index 498dedfe2b4..fd104292f27 100644
--- a/ax/storage/sqa_store/tests/test_sqa_store.py
+++ b/ax/storage/sqa_store/tests/test_sqa_store.py
@@ -751,7 +751,9 @@ def test_ExperimentObjectiveUpdates(self) -> None:
 
         # replace objective
         # (old one should become tracking metric)
-        optimization_config.objective = Objective(metric=Metric(name="objective"))
+        optimization_config.objective = Objective(
+            metric=Metric(name="objective"), minimize=False
+        )
         experiment.optimization_config = optimization_config
         save_experiment(experiment)
         self.assertEqual(
diff --git a/ax/utils/testing/core_stubs.py b/ax/utils/testing/core_stubs.py
index 37343452497..aa3ec939821 100644
--- a/ax/utils/testing/core_stubs.py
+++ b/ax/utils/testing/core_stubs.py
@@ -75,6 +75,7 @@
     TModelPredict,
     TModelPredictArm,
     TParameterization,
+    TParamValue,
 )
 from ax.early_stopping.strategies import (
     BaseEarlyStoppingStrategy,
@@ -439,7 +440,7 @@ def get_test_map_data_experiment(
 def get_multi_type_experiment(
     add_trial_type: bool = True, add_trials: bool = False, num_arms: int = 10
 ) -> MultiTypeExperiment:
-    oc = OptimizationConfig(Objective(BraninMetric("m1", ["x1", "x2"])))
+    oc = OptimizationConfig(Objective(BraninMetric("m1", ["x1", "x2"]), minimize=True))
     experiment = MultiTypeExperiment(
         name="test_exp",
         search_space=get_branin_search_space(),
@@ -505,7 +506,9 @@ def get_factorial_experiment(
         name="factorial_test_experiment",
         search_space=get_factorial_search_space(),
         optimization_config=(
-            OptimizationConfig(objective=Objective(metric=get_factorial_metric()))
+            OptimizationConfig(
+                objective=Objective(metric=get_factorial_metric(), minimize=False)
+            )
             if has_optimization_config
             else None
         ),
@@ -1489,21 +1492,12 @@ def get_augmented_hartmann_metric(
 
 
 def get_factorial_metric(name: str = "success_metric") -> FactorialMetric:
-    coefficients = {
+    coefficients: Dict[str, Dict[TParamValue, float]] = {
         "factor1": {"level11": 0.1, "level12": 0.2, "level13": 0.3},
         "factor2": {"level21": 0.1, "level22": 0.2},
         "factor3": {"level31": 0.1, "level32": 0.2, "level33": 0.3, "level34": 0.4},
     }
-    return FactorialMetric(
-        name=name,
-        # Expected `Dict[str, Dict[typing.Optional[typing.Union[bool, float, str]],
-        # float]]` for 3rd parameter `coefficients` to call
-        # `ax.metrics.factorial.FactorialMetric.__init__` but got `Dict[str,
-        # Dict[str, float]]`.
-        # pyre-fixme[6]:
-        coefficients=coefficients,
-        batch_size=int(1e4),
-    )
+    return FactorialMetric(name=name, coefficients=coefficients, batch_size=int(1e4))
 
 
 def get_dict_lookup_metric() -> DictLookupMetric:
@@ -1556,18 +1550,18 @@ def get_branin_outcome_constraint() -> OutcomeConstraint:
 ##############################
 
 
-def get_objective() -> Objective:
-    return Objective(metric=Metric(name="m1"), minimize=False)
+def get_objective(minimize: bool = False) -> Objective:
+    return Objective(metric=Metric(name="m1"), minimize=minimize)
 
 
-def get_map_objective() -> Objective:
-    return Objective(metric=MapMetric(name="m1"), minimize=False)
+def get_map_objective(minimize: bool = False) -> Objective:
+    return Objective(metric=MapMetric(name="m1"), minimize=minimize)
 
 
 def get_multi_objective() -> Objective:
     return MultiObjective(
         objectives=[
-            Objective(metric=Metric(name="m1")),
+            Objective(metric=Metric(name="m1"), minimize=False),
             Objective(metric=Metric(name="m3", lower_is_better=True), minimize=True),
         ],
     )
@@ -1576,7 +1570,10 @@ def get_multi_objective() -> Objective:
 def get_custom_multi_objective() -> Objective:
     return MultiObjective(
         objectives=[
-            Objective(metric=CustomTestMetric(name="m1", test_attribute="test")),
+            Objective(
+                metric=CustomTestMetric(name="m1", test_attribute="test"),
+                minimize=False,
+            ),
             Objective(
                 metric=CustomTestMetric(
                     name="m3", lower_is_better=True, test_attribute="test"
@@ -1606,7 +1603,9 @@ def get_scalarized_objective() -> Objective:
 
 
 def get_branin_objective(name: str = "branin", minimize: bool = False) -> Objective:
-    return Objective(metric=get_branin_metric(name=name), minimize=minimize)
+    return Objective(
+        metric=get_branin_metric(name=name, lower_is_better=minimize), minimize=minimize
+    )
 
 
 def get_branin_multi_objective(num_objectives: int = 2) -> Objective:
@@ -1677,8 +1676,12 @@ def get_multi_objective_optimization_config(
     )
 
 
-def get_optimization_config_no_constraints() -> OptimizationConfig:
-    return OptimizationConfig(objective=Objective(metric=Metric("test_metric")))
+def get_optimization_config_no_constraints(
+    minimize: bool = False,
+) -> OptimizationConfig:
+    return OptimizationConfig(
+        objective=Objective(metric=Metric("test_metric"), minimize=minimize)
+    )
 
 
 def get_branin_optimization_config(minimize: bool = False) -> OptimizationConfig:
diff --git a/tutorials/factorial.ipynb b/tutorials/factorial.ipynb
index df7ad16d80f..63c559635d6 100644
--- a/tutorials/factorial.ipynb
+++ b/tutorials/factorial.ipynb
@@ -270,7 +270,7 @@
         "    name=\"my_factorial_closed_loop_experiment\",\n",
         "    search_space=search_space,\n",
         "    optimization_config=OptimizationConfig(\n",
-        "        objective=Objective(metric=FactorialMetric(name=\"success_metric\"))\n",
+        "        objective=Objective(metric=FactorialMetric(name=\"success_metric\"), minimize=False)\n",
         "    ),\n",
         "    runner=MyRunner(),\n",
         ")\n",