This report aims to optimize car test drive routes within Kozhikode District, a region in southern India known for its diverse landscapes and traffic patterns. Given the critical role of the test drive in the car purchasing process, streamlining this experience can benefit both customers and dealerships by saving time and providing a more efficient evaluation of vehicle performance. However, the complexity of the district’s road conditions and traffic necessitates an advanced optimization approach. To tackle this challenge, we employ a Genetic Algorithm (GA), a powerful optimization technique inspired by natural selection processes. GAs are renowned for their ability to solve complex problems by mimicking evolutionary principles such as selection, crossover, and mutation. Additionally, we compare the results with those obtained using Simulated Annealing (SA), another optimization method. Our findings indicate that the GA outperforms SA, providing more efficient test drive routes. By utilizing a GA, we aim to identify the most efficient test drive routes considering various factors, including road conditions, traffic density, and distance, thereby enhancing the overall test drive experience for customers and optimizing resource allocation for dealerships.