This study proposes a method, grounded in a multilevel decision-making approach, for a stationary fixed-plate photovoltaic (PV) collector system. The system is comprised of three different subsystems: cell, panel, and array. We consider photovoltaic effects for output performance and an inverter system for distribution from the PV collector, including multiple conflicting objectives in individual subsystems in terms of cell conversion efficiency, power output, incident solar energy, seasonal characteristics, and costs. In terms of the performance in individual subsystems, the problem is reformulated into several smaller subproblems at each subsystem, and a coordination problem at the system level is compromised for optimization purposes. Multilevel optimization for the stationary fixed-plate PV collector system is achieved through the results of single-objective optimization that uses Genetic Algorithm programming (GA) to find global optimum solutions with decision-making under modified game theory. Thus, this work contributes to the optimal design of a stationary fixed-plate PV collector system for the best compromise solution based on specified requirements.