This project aims to develop an automated diagnostic system that leverages genetic algorithms (GAs) for analyzing electroneurodiagnostic (END) data, including electroencephalograms (EEG) and electromyograms (EMG). The growing complexity of END data poses significant challenges for accurate diagnosis and timely intervention in neurological disorders. By utilizing genetic algorithms, we aim to enhance the feature selection process, optimizing the identification of relevant patterns associated with various neurological conditions. The system will undergo rigorous training using a diverse dataset, allowing it to recognize and classify abnormalities effectively. Initial results indicate that GAs can significantly improve diagnostic accuracy compared to traditional methods, reducing the likelihood of misdiagnosis and facilitating early intervention. The project also aims to establish a user-friendly interface for clinicians, enabling them to interpret results intuitively. This innovative approach enhances diagnostic capabilities and contributes to neuroinformatics, promoting the integration of artificial intelligence in clinical practice.