Abstract: Optimal groundwater remediation design problems are often complex, nonlinear, and computationally intensive. Genetic algorithms allow solution of more complex, nonlinear problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). This paper presents a new self­adaptive hybrid genetic algorithm (SAHGA) and compares its performance to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on a groundwater remediation problem. Of the two hybrid algorithms, SAHGA is shown to be far more robust than NAHGA, providing fast convergence across a broad range of parameter settings. For the test problem, SAHGA needs 75% fewer function evaluations than SGA, even with an inefficient local search method. These findings demonstrate that SAHGA has substantial promise for enabling solution of larger-scale problems than was previously possible.