Choosing a suitable location for solar photovoltaic (PV) plants depends on several conflicted selection criteria such as technical, economical, and social restrictions. For handling such a problem, this paper proposes two Computational Intelligence (CI) based frameworks called Genetic Algorithm with Repairing operator (GAR) and Map-Reduce-based Genetic Algorithm with a Repair operator (MRGAR). The governorate of the Red Sea in Egypt is selected as a case study which is a privileged area for harvesting solar energy. Firstly, all gathered maps and geographic information resources are manipulated. Secondly, the regarded problem is formulated as a binary-constrained multiobjective optimization problem. Finally, this problem is solved with two proposed frameworks and the results are simulated. The experimental results conclude that both frameworks are efficient for solving the regarded site selection problem. However, GAR and MRGAR have diversified performances. In particular, the percentage of gathered solar energy of GAR is better than MRGAR whereas MRGAR is significantly faster than GAR. Therefore, MRGAR is recommended to deal with large-scale problems.