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A cooperative control method of multi-class UAV (unmanned air vehicle) team is investigated. During the mission, the UAVs perform search, classification, attack and battle damage assessment (BDA) tasks at various locations, which involves a combination of the team intelligence type of decision making combined with control, estimate and real-time trajectory optimization. The search-theoretic approach based on rate of return (ROR) maps is developed to get the cooperative search strategy. Templates are developed and views are combined to maximize the probability of correct target identification over various aspect angles. Monte Carle simulation runs for the scenario to evaluate the performance of the approach with various decision parameters, UAVs distributions and UAV team characteristics. Simulation results show that the cooperative behavior can significantly improve the operational effectiveness of UAV team, and the cooperative control allows for near optimal solution of the correlative behavior of a group of UAVs in battlefield.