Characterizing acoustic fields over space is required in sound field analysis, spatial audio, as well as several applications within room acoustics and virtual reality. In order to measure a sound field over medium/large volumes, a large number of sensors have to be distributed over space. In this study we investigate optimal distributions of sensors for capturing acoustic fields in space. The positions are selected to maximize the sampled information and minimize the uncertainty in the reconstructed field. We show that the proposed optimization substantially reduces the amount of measurements in comparison to uniform or randomized distributions. The proposed optimal selection procedure can also be significant for other data-scarce applications.