This paper presents an optimization procedure for multi-criterion analysis essential in many biomechanical studies. The optimization is illustrated with a heel-toe running analysis wherein the rate of load and the passive load on support leg are minimized concurrently. The goal of multi-criterion optimization is achieved by incorporating the criterion of Pareto optimality in the genetic algorithm. The proposed procedure can replace the popular weighted-sum approach for problems with multiple objectives. The selection of a final design from the Pareto optimum points (non-dominated designs) can be determined, based on the min-max objective deviation criterion. Nevertheless, a different decision can be made in the final selection without incurring recalculations. The scheme is readily adoptable for parallel computing, which deserves further study to reduce the execution time in a complex biomechanical analysis.