Wind farm layout is a vital subject due to its effect on power generation. Wind farm layout optimization (WFLO) is a complex optimization problem that cannot be solved by traditional optimization methods. Therefore, heuristic optimization techniques are used to solve a WFLO problem. With this problem, the search space plays a significant role in the results. Therefore, this study investigates the impacts of solving the WFLO problem in continuous and discrete search spaces using a genetic algorithm. Besides, Jensen's wake effect model is involved in this study to estimate the velocity deficit within the wind farm. A case study that features a wind profile with multi-speed and multi-direction is used to demonstrate how to get a wind farm layout in discrete and continuous search spaces. The results indicate for the superiority of a continuous search space in terms of compactness, whereas the discrete search space has higher output power, efficiency, and shorter computational time. These results are due to the limitations on the number of generations, population sizes, and the computational machine to get the optimality of the genetic algorithm.