Simulated annealing is introduced and applied to the optimization of groundwater management problems cast in combinatorial form. This heuristic, probabilistic optimization method seeks minima in analogy with the annealing of solids and is effective on large‐scale problems. No continuity requirements are imposed on objective (cost) functions. Constraints may be added to the cost function via penalties, imposed by designation of the solution domain, or imbedded in submodels (e.g., mass balance in aquifer flow simulators) used to evaluate costs. The location of global optima may be theoretically guaranteed, but computational limitations lead to searches for nearly optimal solutions in practice. Like other optimization methods, most of the computational effort is expended in flow and transport simulators. Practical algorithmic guidance that leads to enormous computational savings and sometimes makes simulated annealing competitive with gradient‐type optimization methods is provided. The method is illustrated by example applications to idealized problems of groundwater flow and selection of remediation strategy, including optimization with multiple groundwater control technologies. They demonstrate the flexibility of the method and indicate its potential for solving groundwater management problems. The application of simulated annealing to water resources problems is new and its development is immature, so further performance improvements can be expec
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