Finding a strategy that allows economically efficient drinking water production at minimal environmental cost is often a complex task. A systematic trade-off among the costs and benefits of possible strategies is required for determining the optimal production configuration. Such a trade-off involves the handling of interdependent and nonlinear relations for drawdown-related objective categories like damage to wetland vegetation, agricultural yield depression, reduction of river base flow rates, and soil subsidence. We developed a method for multiple-objective optimization of drinking water production by combining Busacker and Gowen's <1961> minimum cost flow procedure for optimal use of the transport network with a genetic algorithm (GA) for optimization of other impacts. The performance of the GA was compared with analytically determined solutions of a series of hypothetical case studies. Pareto-optimality and uniqueness of solutions proved to be effective fitness criteria for identifying trade-off curves with the GA.