In this work, a holistic approach to optimizing driving behavior by exploiting acceleration-based metrics is proposed. By employing a Genetic Algorithm (GA), a search for smooth and safe acceleration profiles, particularly minimal “jerk,” which is the time derivative of acceleration, that also satisfy practical constraints such as maximum acceleration limits, traveling time, and specific target distance is executed. Contrasting conventional gradient-based optimizers, GA presents the ability to effectively explore a vast and potentially non-smooth solution area, thereby overcoming the risk of converging to local minima or infeasible solutions. A detailed formulation of the optimization problem, reflecting the constraints and objectives incorporated in a GA formulation is presented. Optimization results demonstrate that GA-based solutions identify smooth acceleration profiles, maximize passenger safety, and satisfy key performance metrics, such as comfort, and constraints.