In this paper, a Wave Energy Conversion (WEC) model consisting of a Savonius type wave turbine plus DC generator, Battery Energy Storage System (BESS), inverter, and Inductance-Capacitance-Inductance (LCL) filter is designed. In this study, the optimization techniques are used to maximize Savonius type wave turbine selfefficiency considering different factors such as the height, depth, length, and time period of the wave. Four meta-heuristic optimizations are competing in this article for obtaining the optimum parameter design based on the output electrical power of the wave. These are Whale Optimization Algorithm (WOA), Artificial Immune System (AIS), Bat Algorithm (BA), and Particle Swarm Optimization (PSO) algorithm. The comparative results verify the reliability of WOA in maximizing the overall electrical output power from the wave turbine while confining with the constraints. Moreover, an efficient control system is designed to operate in Islanded Mode (IM) and Grid-Connected (GC) operations according to the matching between the maximum estimated output power from the WEC system and the system load. A proposed control scheme is performed for adjusting the system frequency and voltage in islanded mode operation and ensuring active power-sharing in grid-connected mode. The islanded control scheme consists of three levels; droop control, inner voltage, and current control, outer control based on model predictive control (MPC) to maintain the system frequency and voltage to their nominal values. WOA is also proposed to improve the performance index by optimally evaluate the control signal and tracking error weighted parameters. The GC control scheme is based on the Active/Reactive Power (P/Q) controller to ensure active power-sharing. The effectiveness of the proposed controller has been verified by considering system parameters uncertainties