Assembly line balancing is a key for organizational productivity in terms of reduced number of workstations for a given production volume per shift. Mixed-model assembly line balancing is a reality in many organizations. The mixed-model assembly line balancing problem comes under combinatorial category. So, in this paper, an attempt has been made to develop three genetic algorithms for the mixed-model assembly line balancing problem such that the combined balancing efficiency is maximized, where the combined balancing efficiency is the average of the balancing efficiencies of the individual models. At the end, these three algorithms and another algorithm in literature are compared in terms of balancing efficiency using a randomly generated set of problems through a complete factorial experiment, in which “Algorithm”, “Problem Size” and “Cycle Time” are used as factors with two replications under each of the experimental combinations to draw inferences and to identify the best of the four algorithms. Then, through another set of randomly generated small and medium size data, the results of the best algorithm are compared with the optimal results obtained using a mathematical model. It is found that best algorithm gives the optimal solution for all the problems in the second set of data, except for one problem which cannot be solved using the model. This observation supports the fact that the best algorithm identified in this paper gives superior results.