Applied research into Renewable Energies raises complex challenges of a technological, economical or political nature. In this paper, we address the techno-economical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately , our goal is to build a decision support tool that will provide private and governmental investors into renewable energy systems, valuable insights to make informed short and longer term decisions with respect to park creation and placements. Existing approaches have essentially focused on past data to tackle variations of this problem. In this paper, we introduce a novel approach that considers both past and forecast data, and show the impact for accounting for both sets of data and constraints in a two-stage optimization model. We first show that integer linear programming is best suited to solve the past data model compared to Dynamic Programming and Constrained Local Search. We then introduce our two-stage model that accounts for forecast data as well, adding new constraints to the initial model. Our empirical results show that the two-stage model improves solution quality and overall costs, and can be solved effectively to optimality using ILP.