The coalition formation problem has received a consierable amount of attention in recent years. In this work we present a novel distributed algorithm that returns a solution in polynomial time and the quality of the returned solution increases as agents gain more experience. Our solution utilizes an underlying organization to guide the coalition formation process. We use reinforcement learning techniques to optimize decisions made locally by agents in the organization. Experimental results are presented, showing the potential of our approach.