A cooperative team of agents may perform many tasks better than single agents. The question is how cooperation among self-interested agents should be achieved. It is important that, while we encourage cooperation among agents in a team, we maintain autonomy of individual agents as much as possible, so as to maintain flexibility and generality. This paper presents an approach based on bidding utilizing reinforcement values acquired through reinforcement learning. We further apply evolutionary computation to enhance cooperation among self-interested agents of a team. We tested and analyzed this approach in a variety of task domains, and demonstrated that a team indeed performed better than the best single agent as well as the average of single agents.