This chapter introduces and explains heuristic and meta-heuristic optimization algorithms in detail. It highlights their characteristics, differences, and classifications, helping readers understand how these algorithms work to find optimal solutions in complex problem spaces. The chapter describes important features such as exploration, exploitation, and randomization, and explains how different algorithms achieve a balance between these to improve performance. Both trajectory-based and population-based meta-heuristics are discussed, including Simulated Annealing, Hill Climbing, and the Great Deluge algorithm, along with their strengths, limitations, and pseudocode. The chapter also explores the popularity of population-based approaches like Genetic Algorithms, Particle Swarm Optimization, and Grey Wolf Optimizer due to their effectiveness in exploring large search spaces using multiple agents. Overall, the chapter lays a solid foundation for understanding how meta-heuristics can solve real-world optimization problems across various domains.