The deep-sea unmanned vessel faces the problems of insufficient range and slow convergence and low accuracy of path planning algorithm when exploiting fishery resources.For the purpose of minimizing the environmental impact and optimizing navigation routes of unmanned fishing vessels during the actual mission execution of the unmanned vessel in fisheries, a path planning algorithm with the objective of minimizing multiple parameters such as path length, smooth and current energy was designed under the premise of ensuring its safe navigation.The multi-objective computational model of unmanned vessel under time-varying current disturbance was established by analyzing the ocean environment and mission objectives of the unmanned ship during navigation, and an improved adaptive gray wolf optimization algorithm was employed to settle the problem, which is integrated optimization by introducing multiple strategies. Eventually, the feasibility of the algorithm in the field of multi-objective optimization of unmanned surface vehicles, as well as the usefulness of the improved strategy can be confirmed by simulation experiments. The optimization rate of total objective value was improved by 9.2%, 1.7% and 11.9% for multi-objective compared to three single-objective simulation results. The simulation paths under different current conditions show that the distance-optimal algorithm can save more cost than the traditional distance-optimal algorithm and effectively improve the performance of global trajectory planning.