This study aims to develop a methodology for data collection that accounts for the application of simulation models in decision making for groundwater management. Simulation model reliability is estimated by comparing the effects that a perturbation in the model parameter space has over the model output as well as over the solution of a multiobjective optimization problem. The problem of experimental design for parameter estimation is formulated and solved using a combination of genetic algorithm and gradient-based optimization. Gaussian quadrature and Bayesian decision theory are combined for selecting the best design under parameter uncertainty. The proposed methodology is useful for selecting a robust design that will outperform all other candidate designs under most potential "true" states of the system. Results also show that the uncertainty analysis is able to identify complex interactions among the model parameters that may affect the performance of the experimental designs as well as the attainability of management objectives.