In recent years, the rapid development of unmanned surface vessel (USV) navigation technology has driven a surge of interest in path planning algorithms. As a core technique in this field, traditional evolutionary algorithms have been widely applied to solve USV path planning problems under multiple constraints. However, these algorithms commonly encounter limitations such as low search efficiency and slow convergence. Based on the latest advances in path optimization, this paper proposes a novel stochastic schemata exploiter (SSE) algorithm. SSE adopts the framework of genetic algorithms and uses integer encoding to map feasible paths to chromosomes. It breaks through the traditional selection–crossover model of subpopulation generation by extracting common schemata through semi-ordered permutation and combination strategies to guide offspring evolution. Specifically, SSE integrates schema theory into the iterative process, prioritizing the retention of high-fitness individuals and extracting advantageous schemata. A bounded mutation operation is further employed to enhance search capability, enabling both strong global exploration and rapid convergence. Simulation experiments under multiple constraints—including path length, navigation safety, and path smoothness—demonstrate the effectiveness and superiority of the SSE algorithm. Comparative experiments with genetic algorithm (GA), ant colony optimization (ACO), and the Q-learning algorithm show that SSE achieves significantly better overall performance in path planning tasks.