This paper introduces Genetic IPHA, a novel method for Explainable Artificial Intelligence (XAI) aimed at identifying the importance of pixels in image classification tasks performed by neural networks. The proposed approach formulates interpretability as an optimization problem, employing a genetic algorithm to maximize a fitness function and generate masks that highlight either important or unimportant pixels for model predictions. Experiments were conducted using the CIFAR-10 dataset to evaluate and compare Genetic IPHA against established interpretability methods. The results demonstrate that Genetic IPHA consistently outperforms other algorithms, achieving superior accuracy in identifying both relevant and non-relevant pixels. This highlights its potential as a robust and effective solution for enhancing model interpretability.