Coverage path planning(CPP)is usually applied to large-scale maritime search and rescue(SAR) mission conducted with an autonomous underwater vehicle(AUV)equipped with a side-scan sonar(SSS). There is a large chance of failure while AUV performs a large-scale mission. As a result，apart from completely covering the area，search of the area where the target might be preferentially and collecting of high-quality sensor data are needed. Aiming at this gap，a novel operation-oriented path planner named SAR-A* is proposed to generate a path for improving the coverage efficiency and quality of data obtained. Considering the predicted location and trace by rescue specialist，a two-dimensional Gaussian distribution is introduced to describe the probability map of target presence served as prior information. Moreover，the SAR-A* is developed for selecting the next waypoint from candidate cells. To choose the optimal next waypoint，the next waypoint selection strategy is then transformed into a multi-objective decision-making problem and the weighted metric method is applied to solve the multi-objective optimization. Due to the proposed SAR-A* algorithm，the AUV is guided to the most valuable area with a shorter path that has fewer turn，Simulation results show that the SAR-A* path planner is suitable for large-scale SAR，and can steeply increase confidence in locating the target.