Adaptive Transit Signal Priority (TSP) is increasingly becoming a dominant form of preferential treatment for transit vehicles along urban arterials. The optimization of real-time adaptive traffic signals is both complex and demanding. Conventional optimization methods, including calculus-based, enumerative, and random search methods, lack both the speed and robustness needed for such applications. Parallel Genetic Algorithms (PGAs) have the potential to overcoming these obstacles. This paper applies PGAs to optimize an adaptive strategy for TSP. The results show that the PGAs can offer more efficient and faster optimization for the adaptive TSP strategy in terms of convergence speed and required computation resources, especially for the complex case of congested traffic conditions. Copyright ASCE 2006.