Abstract The motivation for using genetic algorithms (GAs) for transportation optimization problems is due to the globality, parallelism and robustness of GAs. In addition, GAs are simple and powerful in their search for improvement, and not fundamentally limited by restrictive assumption about the search space. Recent related studies using GAs have shown advantages in dealing with non-convexity, locality and complexity of transportation optimization problems, especially in optimal pavement management (Fwa et al., 1994), optimal traffic signal control (Hadi and Wallace, 1993; Memon and Bullen, 1996; Lo et al., 2000), urban transit system design problems (Pattnaik et al., 1998; Chakrobort et al., 1998), aircraft gate re-assignment problem (Gu and Chung, 1999) and origin-destination matrix estimation problem (Reddy and Chakroborty, 1999). In this chapter, we present various applications of GAs to transportation optimization problems. In the first section, GAs are employed as solution algorithms for advanced transport models while in the second section, GAs are used as calibration tools for complex transport models. Both sections show that, similar to other fields, GAs provide an alternative powerful tool to a wide variety of problems in the transportation domain.