Groundwater is considered as an important source of freshwater for a variety of purposes including drinking, domestic, industrial, and irrigation uses. Because of increasing population and life standards, there is a growing need for the optimum utilization of groundwater resources. In this paper, a multiobjective particle swarm optimization model with a new evolutionary strategy based on the compromise solution of the Pareto-front optimal solutions is presented. The advantage of this proposed model stems from using a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process. The new evolutionary strategy is verified on a variety of multiobjective standard test problems with either connected or disconnected Pareto fronts. The proposed multiobjective evolutionary strategy is reminiscent of single-objective optimization, in that its fitness assignment and convergence criteria are both based on tracking a single evolving solution over the search history. Details of the model development and implementation are described and an example application related to groundwater management is presented to demonstrate the capabilities of the proposed model. The proposed model showed its ability to drive the Pareto-optimal solution for the example application and consequently its ability to be applied in real-life groundwater management problems.