The power of Deep Learning Networks has allowed us to build applications far beyond what was thought possible during our time. But the basic forms of these architectures still have their limitations in terms of the data needed and difficulty in understanding and tuning the parameters of these networks. Our project aims to deal with these limitations as effectively as possible. For this reason we have chosen to implement a Siamese Neural Network in order to overcome the requirements of classical Deep Learning based image classification. And in order to further increase the efficiency of the network, we will process it using a genetic algorithm and harness it to improve the architecture and training efficiency by letting the algorithm fine-tune the parameters to get the best possible configuration for the neural network. © BEIESP.