Methods: This study established a home care routing and scheduling model and determined the corresponding spatial optimization problems. The problems were reduced to a multi-depot vehicle path optimization problem, and the nondeterministic polynomial time difficulty of this problem was demonstrated. Results: The results show that the algorithms developed in this study were significantly more effective than the benchmark and random algorithms. Considering the total operating costs of home healthcare institutions, the service-time first greedy algorithm, customized genetic algorithm, and customized tabu search algorithm reduced costs by 45.8%, 53.0%, and 57.7%, respectively, compared with the benchmark algorithm. In addition, the greedy algorithm and customized genetic algorithm, which prioritize service time, improved the service quality of family medical institutions by 31.7% and 65.7%, respectively, compared to the benchmark algorithm, and by 65.7% and 126.3%, respectively, compared to the random algorithm. Conclusions: By unveiling effective resource allocation and spatial optimization strategies for home healthcare in rural China, this study offers valuable insights for policymakers. The findings contribute to informed decision-making, facilitate cost minimization, and maximize service quality in healthcare policy formulation and implementation. Keywords: home care, path planning, resource scheduling, society population