The covariance Matrix Adaptation Evolution Strategy (CMA-ES), introduced more than twenty years ago, has become one of the most powerful and widely used stochastic optimization algorithm for continuous domain. Its ability to handle non-convex, multi-modal, and ill-conditioned problems has made it a method of choice in numerous fields, including machine learning, engineering design, robotics, and even medical and biomedical applications. Yet, despite its broad impact, the internal mechanisms of CMA-ES can appear complex to newcomers. This tutorial provides an accessible introduction to CMA-ES, aimed at participants with little or no prior experience with evolution strategies. We will unpack the core principles behind its robustness and performance: adaptive covariance matrix learning, step-size control, invariance properties, and the role of sampling and recombination. Through intuitive explanations, visual illustrations, and practical examples, attendees will gain a clear understanding of how CMA-ES works, why it is effective on challenging real-world problems, and how to apply it successfully. The tutorial will also cover recommended parameter settings, common pitfalls, and available software tools. By the end, participants will be equipped with both the intuition to use CMA-ES for a wide range of continuous optimization tasks.