The energy-saving optimization problems have gained popularity in recent decades due to their implications for the benefit of the world. In that sense, the Wind Farm Layout Optimization (WFLO) problem has maintained its relevance in evolutionary computation since its inception in the 90s. Essentially, this problem involves the task of allocating the minimum feasible quantity of wind turbines in a constrained wind farm. However, the optimization problem deals with the fact of placing the precise amount of turbines in the correct distribution in order to generate the greatest possible energy at the lowest installation cost. It is well-known that this problem has been addressed by many computational schemes, nonetheless, the evolutionary computation has probed its efficiency over other methodologies in terms of computational resources and robustness. In this study, the Differential Evolution (DE) algorithm and some of its ancient and contemporary variants are analyzed to solve the WFLO problem from the perspective of two different practical cases. The obtained results demonstrate the advantages of evolutionary strategies for solving these types of energy-saving problems such as the capabilities of each DE variant.