Determination of optimal sensor configuration is an important issue in many remote imaging modalities such as tomographic and interferometric imaging. In this paper, a statistical optimality criterion is defined and a search is performed over the space of candidate sensor locations to determine the configuration that optimizes the criterion over all candidates. To make the search process computationally feasible, a modified version of a previously proposed suboptimal backward greedy algorithm is used. Connections of the method to the deterministic backward greedy algorithm for the subset selection problem are presented and a conjecture on the optimality of the proposed method is suggested. Three compelling optimality criteria are introduced and their performance is investigated through numerical experiments for a tomographic imaging scenario. In all cases, it is verified that the chosen configuration by the proposed algorithm performs better than wisely chosen alternatives.