Multi-robot path planning is the premise of the cooperative work of group robots, and it is characterized by the pursuit of multi-resource minimum consumption under the premise of collision prevention and obstacle avoidance. Aiming at this characteristic, a collaborative non-dominant sequencing genetic algorithm is proposed to solve the multi-robot path-planning problem with multiple optimization objectives. An improved multi-objective optimization algorithm is applied to overcome the deficiency of multi-objective optimization in weight selection, and the total path length, smoothness, and time of the multi-robot are considered. At the same time, a collaborative algorithm framework is introduced to solve the problem of multivariate groups; here, each robot path is regarded as a sub-population. Parts of the sub-populations are selected by the non-dominated sorting genetic algorithm (NSGA-Ⅱ) with elitist strategy, and they evolve into cooperative coevolution. Each iteration update outside the elite solution set eventually forms a set of control path. Simulation results show that the proposed algorithm can effectively realize multi-objective path planning for multi-mobile robots in a known raster map environment.