This article presents a comprehensive analysis of the application of evolutionary algorithms in optimizing adaptive learning systems. As education systems increasingly shift towards personalized learning environments, adaptive learning systems (ALS) have emerged as crucial tools to meet the diverse needs of learners. Evolutionary algorithms, inspired by natural evolutionary processes, offer potent solutions for optimizing various elements of ALS, such as content customization, learning paths, and resource allocation. This study reviews the most prominent evolutionary algorithms, including genetic algorithms, ant colony optimization, and multi-objective optimization algorithms, analyzing their effectiveness in different aspects of adaptive learning. The analysis reveals that while these algorithms significantly enhance the efficiency and personalization of learning experiences, challenges such as convergence issues and computational complexity remain. This article also identifies gaps in the existing literature and proposes directions for future research, emphasizing the need for long-term studies and the integration of evolutionary algorithms with other artificial intelligence methods.