Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. Conventional optimization methods, including calculus-based, enumerative, and random search methods, lack both the speed and robustness needed for such applications. PGAs have the potential to overcome these obstacles. This paper describes an effort to apply Parallel Genetic Algorithm (PGA) to optimize an adaptive strategy for traffic signal control. A simulation testbed using the VISSIM microscopic traffic simulation model was developed to evaluate the proposed PGA application by comparing it with the fully actuated NEMA traffic signal control. The results show that PGA can offer more efficient and faster optimization for the adaptive strategy in terms of convergence speed and required computation resources, especially under the more complex congested traffic conditions. The simulation results show that the PGA-based optimizer for adaptive traffic signal control outperformed the fully actuated NEMA control in all test cases.