An intelligent search strategy based on predictive control and an exploration and exploitation sparrow search algorithm (EESSA) is proposed to address the cooperative search problem of multiple flight vehicles.First，the task area is gridded，and the target existence probability map and information certainty map are used to model the task area.Then，the idea of model predictive control (MPC) is adopted to predict the future flight paths of the vehicles for cooperative search over a certain period.The probability of target existence and the certainty of information are used to quantify the predicted flight paths，and the online decision-making problem of the multiple flight vehicles is modeled as an optimization problem.Finally，the sparrow search algorithm (SSA) is used to obtain the intelligent search decisions.To address the shortcomings of SSA in terms of global optimality and convergence speed when dealing with complex optimization problems，the Tent chaotic mapping and elite back propagation learning strategy are introduced to enhance the diversity of the initial population.The golden sine strategy is adopted to update the positions of the producer sparrows and improve the algorithm’s ability in escaping from local extrema.The positions of scroungers are updated by integrating the concept of exploration and exploitation.Additionally，the cosine strategy and greedy algorithm are utilized to optimize the number of scouter sparrows and update the offspring population，accelerating the convergence speed of the algorithm.Simulation analysis verifies that the proposed algorithm effectively improves cooperative search efficiency.