A continuously rising load demand places an increasing pressure and voltage decrease on the current power distribution network. Due to previously unheard-of issues, including a supply–demand gap, growing costs, and global warming, the power supply sector urgently needs reform. This, in turn, highlights the significance of a smart grid. The smart grid includes generating integration at the distribution level as one of its features. If sized and located properly, distributed generation (DG) may significantly reduce power losses. This chapter describes the application of a genetic algorithm to reduce distribution losses in a feeder by maximizing the size and placement of DG at an existing radial distribution system that represents load with wind production linked to the substation. The performance of a Genetic Algorithm (GA) depends on several factors that must be accurately calibrated. The work in this article is an effort to address this connecting issue. With a voltage-dependent load model, the real radial distribution system is taken into consideration. In this chapter, the ideal placement and size of the DG are determined by experimenting with different GA operator combinations while maintaining constant values for factors like population, crossover percentage, and generation. To examine the impact on active and reactive power loss, the best placement and size are implemented for the least amount of loss. In the first instance, the available generation is employed, and GA determines the ideal position; in the second instance, both the optimal placement and size are implemented. The test findings show a 56.49% decrease in loss if the existing DG is linked at the ideal location as per GA and a 91.47% reduction in loss if location and size are compared to the existing DG, respectively. The tail-end voltage and power losses are both significantly improved using the evolutionary algorithm GA.