Understanding the neurophysiological mechanisms underlying human movement control is pivotal for enhancing quality of life through device development and rehabilitation protocols. Neuromusculoskeletal models (NMS) offer a non-invasive computational approach for probing human biomechanics. Leveraging these models, we propose an optimization procedure employing OpenSim Moco and experimental electromyography (EMG) data to refine model muscular parameters, specifically targeting knee flexion-extension movement. An experiment involving a healthy male subject performing knee flexion-extension movements provided kinematic and myoelectric data. We employed OpenSim’s predictive simulation capabilities and formulated an optimal control problem to minimize the deviation between model and experimental muscle activations. The experimental data guided model scaling and optimization, ensuring anthropometric and muscular fidelity to the subject. Results indicated successful alignment between model and experimental knee movements, with root mean square errors within acceptable ranges. Muscle activation patterns obtained from the optimized model closely mirrored experimental data, validating the optimization approach. Notably, gluteus maximus exhibited lower activation compared to psoas, reflecting the seated posture. Knee flexors and extensors exhibited expected activation patterns throughout the movement cycle. This optimization framework, realized through OpenSim Moco, demonstrates the feasibility of refining NMS parameters using experimental data, thereby enhancing model accuracy. The proposed methodology facilitates personalized biomechanical simulations, crucial for designing rehabilitation protocols and neurorehabilitation strategies tailored to individual needs. Furthermore, it underscores the utility of computational tools in advancing our understanding of human movement dynamics and motor control mechanisms.