Autonomous agents that possess distinct expertise but lack proper coordination skills can suffer from poor performance in a cooperative setting. The success of agents in multiagent systems is based on their ability to adapt effectively with other agents in completing their tasks. We present here a co-evolutionary approach to generating behavioral strategies for autonomous agents cooperating with each other to achieve a common goal. We co-evolve agent behaviors with genetic algorithms (GAs) where one GA population is evolved per individual in the cooperative group. Groups are formed by pairing strategies from each population and the best pairs are stored in shared memory. Population members are evaluated by pairing them with representatives of other populations in the shared memory. Experimental results obtained by conducting experiments in a room painting domain are presented, showing the success of the shared memory approach in consistently generating optimal behavior patterns. Performance comparisons with a random pairing approach and a single population approach demonstrate the utility of the shared memory approach.