This paper explores the use of genetic algorithms as a tool to improve curve data analysis. The genetic algorithm is a search algorithm inspired by the process of natural selection, and it has proven effective in solving optimization problems in various fields. The author used genetic algorithm to estimate the activation energy and frequency factor of the optimization curve, which are considered. The results showed that the genetic algorithm can improve data analysis processes and reach solutions to problems with high accuracy and efficiency compared to traditional methods. This approach can also deal with noisy data and reduce the impact of outliers on the estimation process. Furthermore, the author demonstrated that the genetic algorithm can be generalized to different types of fluorescence curves, such as those generated by different materials or under different experimental conditions. The proposed method is fast, accurate, and robust, making it useful for dosimetry researchers who need accurate estimates of these parameters.