Identifying the principles inherent to human motor control, and the variables that are optimized during movement, has been a challenge for over a century. Several models have been developed, where different variables of the movement are optimized, however, the jerk minimization model has gained some prominence as its output closely matches human movements. However, this model was developed for simple point-to-point 2D movements. To generalize this model, recent studies have developed the Movement Element Decomposition Method (MED) which consists of a process of atomizing complex movements. Still, MED used the same boundary conditions that were used in the initial jerk minimization model of 2D point-to-point movements, making MED output not optimal for continuous movements. The goal of this study was to generalize the jerk minimization model for application to general continuous movements, through the combination of MED and a genetic algorithm model. The curve found in this study has a better fit for continuous movements than the theoretical curves available in the literature. Furthermore, the application of optimization models of motor control variables through the usage of a genetic algorithm enables their integration with other computational tools for more complex modeling.