Nowadays, many Home Health Care (HHC) organizations are increasingly focused on optimizing their services to meet the growing demand for home care assistance. In this context, home health care providers are confronted to multiple objectives such as minimizing the service time and the visit time preferences, reducing the cost service, etc. Based on a literature review, we observe that existing research often addresses this problem using a mono-level decision-making process. However, the problem is naturally structured across two decision levels: a planning decision process associated with caregiver scheduling and a routing entity for transportation optimization. To address this, we extend our previously proposed bi-level mathematical model, and we introduce a novel bi-level evolutionary algorithm, called BiGA-HHCSRP, specifically designed to solve the HHC staff routing and scheduling problem. This approach operates through two hierarchical evolutionary processes: (1) a scheduling evolutionary algorithm to allocate effectively caregivers to patient and (2) a routing evolutionary algorithm to optimize the transportation routes. The proposed algorithm integrates nurse qualifications, travel costs, and patient preferences to generate solutions that enhance patient satisfaction while ensuring alignment with caregiver qualifications. The main motivation behind this work is to leverage the bi-level resolution framework in the context of HHC to improve decision-making efficiency. Statistical experimental comparisons against a classical Genetic Algorithm (GA) in a mono-level decision-making process demonstrate the outperformance of our approach in delivering high-quality solutions.