Summary: The rapid advancement of unmanned aerial vehicles (UAVs) has led to widespread application in cooperative target search. As task scenarios become more complex, the existing search methods struggle to respond promptly to dynamic environments, which limits task efficiency. This paper proposes a cooperative target search framework named Voronoi-based dynamic partition with ant colony optimization (VDP-ACO) to improve search efficiency in dynamic and uncertain environments. A multi-attribute information map to characterize the environmental situation, which helps UAVs better perceive the external environment, is established first. Then, we defined the task benefit and constraint model within the distributed model predictive control (DMPC) framework. To accelerate the solving efficiency, we used ant colony optimization (ACO) to obtain the optimal input, maximizing the task benefit while ensuring the security between the UAV and the external environment. Besides, a dynamic region division strategy based on Voronoi diagrams to balance the uncertainty between sub-regions and reduce frequent transitions during the search process is designed by comprehensively considering the UAV’s position and environmental information. To verify the effectiveness of the proposed algorithm, we used actual terrain data as the task scenario and validated it in different situations. The simulation results show that the proposed method can meet task and UAV safety requirements while improving search efficiency.