Home health care (HHC) management needs to plan their operations to synchronize professionals and allocate resources to perform several HHC services needed by patients. The growing demand for this type of service dictates the interest of all the stakeholders (professionals and patients) in finding high-quality daily solutions and logistics. Routing and scheduling are problems of combinatorial nature, extremely complex, and require sophisticated optimization approaches. This work aims to contribute to cost-efficient decision-making in the general improvement of the service quality. Thus, a mixed integer linear programming model, a genetic algorithm, and a hybrid approach were used to solve the operational planning through test instances of different sizes for public home care providers. Computational results are presented, followed by a discussion on the advantages and shortcomings, highlighting the strength of each approach.