Large Language Models (LLMs) have shown remarkable performance across diverse natural language processing tasks in recent years. However, optimizing instructions to maximize model performance remains a challenge due to the vast search space and the nonlinear relationship between input structure and output quality. This work explores an alternative prompt optimization technique based on genetic algorithms with different structured mutation processes. Unlike traditional random mutations, our method introduces variability in each generation through a guided mutation, enhancing the likelihood of obtaining better prompts for each generation. We apply this approach to emotion detection in the context of SemEval 2025 Task 11, demonstrating the potential to improve prompt efficiency, and consequently task performance. Experimental results show that our method yields competitive results compared to standard optimization techniques while maintaining interpretability and scalability.