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Three optimization models are proposed to select the best subset of stations from a large groundwater monitoring network: ͑1͒ one that maximizes spatial accuracy; ͑2͒ one that minimizes temporal redundancy; and ͑3͒ a model that both maximizes spatial accuracy and minimizes temporal redundancy. The proposed optimization models are solved with simulated annealing, along with an algorithm parametrization using statistical entropy. A synthetic case-study with 32 stations is used to compare results of the proposed models when a subset of 17 stations are to be chosen. The first model tends to distribute the stations evenly in space; the second model clusters stations in areas of higher temporal variability; and results of the third model provide a compromise between the first two, i.e., spatial distributions that are less regular in space, but also less clustered. The inclusion of both temporal and spatial information in the optimization model, as embodied in the third model, contributes to selection of the most relevant stations.