To address the collaborative search task planning issue for unmanned aerial vehicles （UAV） and unmanned ground vehicles （UGV） in search and rescue scenarios， a method that combines centralized task allocation with independent path planning was proposed. Firstly， tasks were clarified based on requirements and assigned to UAV and UGV. Firstly， the UAV and UGV conducted trajectory planning and path planning independently according to their assigned tasks. For the task allocation problem，a multi-objective area segmentation and allocation model based on task types was proposed，and an adaptive hybrid algorithnm incorporating genetic algorithm and tabu search was developed in solution.For trajectory planning， a “traveling salesman path-area coverage path planning” model was constructed based on the task requirements of the UAV， employing an improved genetic algorithm that introduced an “S”-shaped path as the initial population and designed a fitness function to optimize the selection process. Regarding the path planning for UGV， a method that combines A* algorithm with artificial potential field algorithm was proposed to find the optimal traveling path. Simulation results indicate that the proposed algorithm effectively completes search tasks and shows significant improvements in both task execution efficiency and path planning accuracy compared to commonly used genetic algorithms and A* algorithms.