This paper presents an algorithm for optimization. This algorithm is based on a team of learning stochastic automata. Each automaton is characterized by two actions providing a binary output (0 or 1). The action of the team of automata consists of a digital number which represents the environment input. The probability distribution associated which each automaton is adjusted using a modified version of the Bush-Mosteller reinforcement scheme. This adaptation scheme uses a continuous environment response and a time-varying correction factor. A normalization procedure is used in order to preserve the probability measure. The asymptotic properties of this optimization algorithm are presented. A numerical example illustrates the feasibility and the performance of this optimization algorithm.