Genetic Algorithms (GA) are, conceptually, suitable to optimize the Assembly Sequence Planning (ASP) problem. GA was implemented in this research to optimize the ASP problem because they can easily handle large search spaces, flexibility in defining the constraints and derive them in a fitness function. A penalty function approach has been used to compute the fitness value for assembly sequences. The penalty function approach was chosen as the penalties are easy to define, realistically capture the difficulties associated with the assembly process and the number of penalties to consider is relatively reduced. The evaluation of the penalty function is simple and straightforward, a most desirable feature for a population-based search. © 2006-2014 Asian Research Publishing Network (ARPN).