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Solving multiobjective optimization problems requires suitable algorithms to find a satisfactory approximation of a globally optimal Pareto front. Furthermore, it is a computationally demanding task. In this paper, the grid implementation of a distributed multiobjective genetic algorithm is presented. The distributed version of the algorithm is based on the island algorithm with forgetting island elitism used instead of a genetic data exchange. The algorithm is applied to the allocation of booster stations in a drinking water distribution system. First, a multiobjective formulation of the allocation problem is further enhanced in order to handle multiple water demand scenarios and to integrate controller design into the allocation problem formulation. Next, the new grid-based algorithm is applied to a case study system. The results are compared with a nondistributed version of the algorithm.