The necessities of biomechanical computer simulation technologies in real-life medical practices have dramatically increased with high computational complexity, resulting in long computation time even on a modem high-end processors. As the complexity of musculoskeletal models continues to increase, so will the computational demands of biomechanical optimizations. For this reason, parallel computing for biomechanical optimizations is becoming more common. Our study introduces novel parallel algorithms for biomechanical optimization problems, and investigates their performance on a cluster of computers. The parallel algorithms are developed to satisfy computational requirements not readily supported by cutting-edge, high-performance, single-processor systems. By evaluating different parallel algorithms, an efficient parallel decomposition method is proposed for biomechanical optimization problems. An adaptive asynchronous parallel algorithm is proposed for the particle swarm global optimization method in order to overcome load imbalance problems that occur frequently in parallel computing with a synchronous optimization approach. In addition, the proposed adaptive parallel algorithm is applied to a large-scale complex human movement prediction problem. The proposed parallel algorithms are implemented on scalable clusters of computers and performance issues are examined comparatively in terms of several critical factors such as parallelization and optimization methods. The performance results demonstrate that these parallel techniques may provide feasible and efficient solutions for human movement problems, which exhibit high computational cost.