Given the inherent uncertainty in groundwater management problems uncertainty in determining aquifer parameter values, identifying an optimal remediation strategy based on a deterministic description of the system may not yield an optimal and feasible design. This work builds on the robust genetic algorithm (GA) developed by Chan Hilton and Culver. The robust GA is a simulation-optimization approach which combines a GA with a contaminant fate and transport simulation model and a spatially correlated random field generator to identify tradeoffs between design cost and reliability while considering uncertainty of hydraulic conductivity values. This work evaluates the application of the robust GA to two formulations of a groundwater remediation design problem. In this problem, the objectives are to minimize the cost of the remediation design while satisfying water quality constraints and indirectly maximizing the reliability of the designs. This is done by identifying the location and pumping rates of a set of extraction well used for pump-and-treat remediation. The results show that the robust GA can successfully identify cost-effective and reliable designs in a computationally efficient manner. Future work involving the robust GA and planned modifications also are discussed in this paper.