In order to enable the unmanned surface vehicle (USV) for water environment monitoring to effectively avoid static obstacles while monitoring and sampling water quality, and to travel in optimal or near optimal path, we propose a hybrid strategy with variable  step sizes and variable  view ranges, which combines an adaptive artificial fish swarm algorithm (AFSA) with an improved genetic algorithm (GA). After the artificial fish finishes  foraging, tracking and clustering operations, the GA is introduced. A decaying exponential function is chosen to enlarge the vision range of and step length in the early phase and to reduce them in the late phase. This can improve the efficiency and accuracy of the algorithm. Simultaneously, an elite selection strategy, a protection operator and an elimination operator are introduced to the basic genetic algorithm to obtain the global optimal solution. Simulation results show that the hybrid algorithm can effectively overcome the disadvantages of a single algorithm, and that is easy to fall into local convergence. The proposed algorithm has a fast convergence speed and effectively obtains the optimal path with high calculation accuracy.