This chapter explores genetic algorithms as optimization tools inspired by natural evolution. We present both theoretical principles and practical implementations. The material progresses from basic concepts to advanced techniques. Students learn chromosome representation, fitness evaluation, selection mechanisms, and genetic operators. Python examples demonstrate implementation strategies for various problems. Real-world applications include portfolio optimization and constraint handling. Performance analysis helps students understand algorithm behavior.