Side-Channel Analysis (SCA) exploits physical vulnerabilities in systems to reveal secret keys. With the rise of Internet-of-Things, evaluating SCA attacks has become crucial. Profiling attacks, enhanced by Deep Learning-based Side-Channel Analysis (DLSCA), have shown significant improvements over classical techniques. Recent works demonstrate that ensemble methods outperform single neural networks. However, almost every existing ensemble selection method in SCA only picks the top few best-performing neural networks for the ensemble, which we coined as Greedily-Selected Method (GSM). This method of selecting DNN may not be optimal. In this work, we propose a new genetic algorithm-driven ensemble selection algorithm called Evolutionary Avenger Initiative (EAI) to create effective ensembles for DLSCA. We investigate two fitness functions and evaluate EAI across four datasets, including AES and Ascon implementations. We show that EAI outperforms GSM, recovering secrets with the least number of traces. Notably, EAI successfully recovers secret keys for Ascon datasets where GSM fails, demonstrating its effectiveness.