Hand force capacities reflect an individual's ability to generate forces in all directions, considering a given upper-limb posture. These capacities are described as polytopes by means of an upper-limb musculoskeletal model. However, such a model needs to be adapted to an individual for more accuracy. The model parameter space is investigated using derivative-free algorithms which do not require the optimization function to be differentiable: genetic algorithms and SRACOS, a classificationbased algorithm. Results demonstrate that employing a genetic algorithm with a polytope representation in 26 vertices yields the most accurate prediction of force capacities in a validation posture.