Traffic signal synchronization is very important in urban traffic management as it plays a very crucial role with congestion reduction and improves flow. The conventional methods usually have deficiencies since they are not adaptive to dynamic conditions. This paper focuses on a heuristic-based approach for optimizing traffic signal synchronization in an effort to reduce congestion and lower travel times and emissions in cities. A new method is proposed including initialization, evaluation, selection, crossover, mutation, and replacement steps. This can be seen as the dynamic adjustment of the signal timings by real-time traffic data, realized by a straightforward yet effective heuristic algorithm. In a simulated environment with real traffic data, the algorithm was in use and shows improvement in major traffic metrics: travel time, total delay, stops, and emissions by about 25–30%. These results yield evidence that the proposed approach surpasses traditional methods and other current algorithms and can be further adopted as an adaptable and low-cost solution for most cities, where any extensive smart infrastructure is lacking.