Due to the high mobility of Unmanned Aerial Vehicle (UAV), it can be an effective method for pollution detection of vessels on the sea. How to optimize the flight path of the UAV so that the visited energy consumption is minimized is a problem that remains to be solved. In this paper, the Lin-Kernighan-Helsgaun-based trajectory planning method (LKH-TPM) is used to solve the UAV scheduling problem to minimize the UAV visit path length and compare it with the ant colony (ACO) algorithm, simulated annealing (SA) algorithm and tabu search (TS) algorithm. The experiments are carried out under different ship numbers, different sea areas, and different base station numbers, and it is verified that LKH-TPM is a more effective solution for the problem under study.