In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.