In this paper, we propose an optimized version of the Principal Directions algorithm by integrating Genetic Algorithms (GAs) and the k-Nearest Neighbors (KNN) technique, with applications in image class classification. The algorithm enhances the step of quantifying the disturbance introduced to the principal directions when new images are added to existing image classes. The optimal number of principal directions required for effective classification is determined using a genetic algorithm, while an improved version of the KNN algorithm is employed to optimize the key parameter - the number of nearest neighbors - used during testing. For validation and testing, we apply the proposed method to the classification of face image classes corresponding to multiple individuals. Several variants of the algorithm are developed, differing in the similarity measure used within the KNN algorithm and the type of crossover operator employed during binary gene recombination (ex. one-point, two-point, or multi-point crossover). Experimental results demonstrate that the proposed approach achieves high accuracy in recognizing new face images, validating its effectiveness.