 This paper proposes a modified genetic algorithm GA that focuses on minimizing the randomization of the objective function and maximizing accuracy. The algorithm was tested on the egg holder and rastergen functions and compared against other GA algorithms such as the classic GA, genetic evolutionary GA, GACA, weighted optimal algorithm, WOA, self-adaptive GA, SMA, and simulated annealing, SA. The results showed that the modified GA outperformed all other GA algorithms in terms of accuracy and convergence rate. Additionally, it was found that the modified GA had a significantly lower computational cost than the other GA algorithms. This article was authored by Rafal Kisek, Stanislav Kackel, and Adam Kozakovic.