Humans consistently coordinate their joints to perform a variety of tasks. Computational motor control theory explains these stereotypical behaviors using optimal control. Several cost functions have been used to explain specific movements, which suggests that the brain optimizes for a combination of costs and just varies their relative weights to perform different tasks. In the case of tunable human-machine interfaces, we hypothesize that the human-machine interface should be optimized according to the costs that the user cares about when making the movement. Here, we study how the relative weights of individual cost functions in a composite movement cost affect the optimal control signal produced by the user and the mapping between the user's control signals and the machine's output, using prosthesis control as a specific example. This framework was tested by building a hierarchical optimization model that independently optimized for the user control signal and the virtual dynamics of the device. Our results indicate the feasibility of the approach and show the potential for using such a model in prosthesis tuning. This method could be used to allow clinicians and users to tune their prosthesis based on costs they actually care about; and allow the platforms to be customized for the unique needs of every patient.