In this work, we introduce crowdsourcing home healthcare service (CHHS) systems, where caregivers (including nurses, personal care attendants, and housekeepers) from different locations (rather than centralized institutions) offer diverse home healthcare services to caretakers at home. Powered by cloud computing, the CHHS system enables real-time, dynamic, and large-scale matching between caretakers and caregivers based on their preferences and constraints, and determines caregivers’ rostering and routing plans, involving the NP-hard nurse rostering problem (NRP) and the vehicle routing problem (VRP). This work firstly creates a mathematical programming model to jointly roster and route for the CHHS, maximizing the matching scores of caregivers and caretakers based on the preferred features through analytic hierarchy process (AHP), and minimizing caregivers’ overtime and routing costs, under constraints of caregiver skills, regulations, and vehicle routing. The proposed matching score mechanism assigns weights to caretaker preferences, enhancing pairing with preferred caregivers and reducing dissatisfaction. This work proposes a hybrid genetic algorithm with variable neighborhood search (GAVNS), respectively tailored to handle the rostering and routing aspects of CHHS. Simulation indicates that the GAVNS lowers costs by approximately 38% and 26% in rural and city cases, respectively, and outperforms standalone GA and VNS, achieving a 3% additional cost reduction and consistently yielding feasible solutions.