In order to solve the problem of global planning of multi-unmanned vehicle coverage paths in irregular areas, a regional coverage method based on stepwise genetic algorithm is proposed. First, the target area is rasterized according to the size of the cleaning vehicle, and the multi-vehicle area coverage path planning problem is transformed into a multi-travel agent (MTSP) problem. Then, the multi-traveler problem is solved by using the stepwise genetic algorithm. The first step is to transform the multi-traveler problem into the multi-traveler (TSP) problem by using the fuzzy C-means clustering method. In the second step, a stepwise genetic algorithm is used to solve each single traveling salesman problem, and the selection mechanism of the genetic algorithm is improved by using the idea of neutron parent coexistence of weed invasion algorithm. Finally, simulation experiments are carried out in the simulated campus scene and community scene respectively. The experimental results show that the proposed method in the two scenarios can achieve multi-unmanned cleaning vehicles to complete the regional path coverage, and the proposed step-genetic algorithm has a faster convergence rate than the grouping genetic algorithm. In campus scenarios, the proposed stepwise genetic algorithm is 54% less time-consuming and 38% less optimal solution path length than the grouped genetic algorithm. In the cell scenario, the proposed stepwise genetic algorithm reduces the time consumption by 55% and the optimal solution path length by 44% compared with the grouped genetic algorithm.