Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have been shown to be a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognized to be crucial, but so far it has never been thoroughly quantified. Here we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping that exchanges only corresponding agents between teams and free agent swapping that allows an arbitrary exchange of agents. Our results show that restricted agent swapping suffers from premature convergence, whereas free agent swapping entails insufficient convergence. Consequently, in both cases the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem we propose to combine the two methods. Our approach first applies free agent swapping to explore the search space and then restricted agent swapping to exploit it. This mixed approach turns out to be a much more efficient strategy for the evolution of team compositions compared to either strategy alone. Our results suggest that such a mixed agent swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown.