This article explores the use of a genetic algorithm to train a self-parking car. The algorithm creates a first generation of cars with random genomes, which through generations, learn to park themselves. By the 40th generation, the cars start to understand the concept of self-parking and get closer to the parking spot, although they may still hit other cars or not fit perfectly. A simulator is available to demonstrate the evolution process, and the article provides a step-by-step breakdown of how the genetic algorithm is implemented in TypeScript to solve the low-level optimization problem of finding the optimal combination of parameters for self-parking.