In this paper we explore a randomized alternative for the optimization of hybrid systems\u27 performance. The basic approach is to generate samples from the family of possible solutions, and to test them on the plant\u27s model to evaluate their performance. This result is obtained by first presenting the general hybrid optimal control problem, and then converting it into an optimization problem within a statistical learning framework. The results are applied to examples already existing in the literature, in order to highlight certain operational aspects of the proposed methods
