The selection process of the kernel parameters and the relevant features are very crucial to enhance the classification tasks. Thus, in this work, a genetic algorithm that mimics the biological evaluation is used to optimize the support vector machine kernel parameters in order to achieve a high classification accuracy of an acute leukemia diagnosis. The results proved that the combination of genetic algorithm with support vector machine increased the classification accuracy of acute leukemia diagnosis to 99.19%, compared with the value of 89.43% obtained under default support vector machine kernel parameters. This can be directly attributed to the elimination of the irrelevant features and the suitable selection of the kernel parameters. This implies that the genetic algorithm model can be adequately used to solve the optimization problem and features subset selection that gives the optimal accuracy.