A reliable-charging network is urgently demanded to support electrified ride-sourcing services due to their shorter dwell time, longer daily vehicle miles traveled, and concerns of sacrificing revenue for charging activities. We developed an integer programming (IP) model for the optimal allocation of charging stations and charging plugs to minimize the total investment costs and spatio-temporal varying drivers’ value of time (VOT) for charging activities. The trip chain data of the RideAustin ride-sourcing services have been used as a test case, based on which we estimated the charging needs of ride-sourcing EVs and identified candidate charging locations to fulfill the daily travel needs of ride-sourcing drivers. Through numerical study and sensitivity analyses, we analyze the impacts of different charger types, fleet sizes, government incentives, and VOT considerations on the optimal investment plans and system costs, and show the importance of considering ride-sourcing drivers’ VOT into charging infrastructure planning.