Integrating photovoltaic (PV)-based power systems with electric vehicle (EV) charging loads makes perfect sense due to the similarities between them in terms of power form, interface, and locations. However, this integration involves a high level of uncertainty, due to the random nature of solar generation and EV charging, which requires a careful control design and optimization to keep the system stable. This paper presents an optimization algorithm to find the best combination of the control parameters of a voltage source inverter that integrates a PV power system with an EV charging station through a common grid-connected ac-bus. The controller parameters are optimized using Salp Swarm Algorithm to minimize the fluctuation in the dc-bus voltage through balancing the active power-flow and the injected harmonics to the grid. The controller performance under different severe disturbances from both generation and load sides is investigated and compared with an optimum control design using system transfer functions and particle swarm optimization. A hypothetical level 2 ac charging station for EVs is modeled considering different operating conditions and utilized to test the controller along with real-world irradiance profiles. The proposed controller is validated using simulation and processor-in-the-loop platforms. The outcomes show that the proposed control design is able to reduce the fluctuation of the dc-bus voltage by around 50%, the total harmonic distortion of voltage by 40% and current by 64% compared to the analytical-based design, which makes the system compatible with the requirements defined by the IEEE 1547 international standard.