The available highway alignment optimization algorithms use the total cost as the objective function. This is a single objective optimization process. In this process, travel-time, vehicle operation, accident, earthwork, land acquisition, and pavement construction costs are the basic components of the total cost. This single objective highway alignment optimization process has limited capability in handling the cost components separately. Moreover, this process cannot yield a set of alternative solutions from a single run. This paper presents a multi-objective approach to overcome these shortcomings. Some of the cost components of highway alignments are conflicting in nature. Minimizing some of them will yield a straighter alignment; whereas, minimizing others would make the alignment circuitous. Therefore, the goal of the multi-objective optimization approach is to handle the trade-off amongst the highway alignment design objectives and present a set of near optimal solutions. The highway alignment objectives, i.e., cost functions, are not continuous in nature. Hence, a special genetic algorithm based multi-objective optimization algorithm is suggested. The proposed methodology is demonstrated via a case study at the end.