[Purposes] To enhance the efficiency of urban traffic police patrol operations and maintain continuous vigilance over sudden road incidents, [Methods] this study considers factors such as the maximum travel distance of police vehicles during emergency response, road segment delays, and work balance between police vehicles. The objective is to maximize the coverage of graded hotspot areas. An optimization model for traffic police patrol routes is developed under constraints including daily duty requirements and limited police resources. The model is solved using a multi-objective genetic algorithm, with an enhanced multi-objective co-evolutionary method. Finally, a simulation experiment is conducted using the road network of Furong District in Changsha City. [Findings] The results indicate that the derived daily patrol routes meet duty requirements and achieve 100% coverage of high-risk accident hotspot areas. Compared with traditional genetic algorithms, the improved genetic algorithm demonstrates superior solution speed and robustness across road networks of different scales. [Conclusions] By efficiently covering urban hotspots, this approach can effectively enhance police emergency preparedness and improve the utilization of police resources, providing valuable insights for the deployment of urban traffic police patrol routes.