<p>Effective decision support for cooperative target search operations is crucial in unmanned aerial vehicle (UAV) swarm applications, such as disaster response and environmental monitoring. Determining and dynamically presenting the optimal local path for the UAV swarm within a finite time horizon is a key factor in ensuring decision-making efficiency. Typically, the “search return” of a path is constructed using the target existence probability and calculated by the Bayesian formula; however, it may be outweighed by other metrics, such as flight costs, thereby reducing the overall search efficiency. Furthermore, existing optimization models often overlook the decision latency inherent between the decision-maker (DM) and unmanned aerial vehicles (UAVs). To address these limitations, this paper proposes a cooperative target search path planning method for heterogeneous UAVs (search and relay UAVs) within a distributed model predictive control (DMPC) framework. The proposed approach integrates objective functions balancing conflicting metrics like search return, communication quality and flight cost. An adapted Differential Evolution algorithm incorporating a Chebyshev distance heuristic, termed CDIDE, is developed to solve the proposed model. Three simulation cases based on real-world terrains are designed to validate its performance. Ablation studies indicate that the proposed Chebyshev distance heuristic improves search efficiency by 51.4%–75.4% compared to the baseline variant. Rank-sum tests conducted across five performance metrics confirm that CDIDE significantly outperforms recent comparison methods. These findings enhance intelligent decision-making by offering a quantifiable trade-off analysis among search efficiency, resource consumption, and decision latency, which is critical for the deployment of models and optimizers in related intelligent agents.</p>