<p>Developing a high-performance elevator group control system (EGCS) remains a challenging problem in the elevator industry. Aiming to achieve optimal transportation efficiency and reduced passenger waiting time, this paper proposes an optimized scheduling strategy for elevator groups that integrates evolutionary computation with deep neural networks (DNNs). First, the overall dispatching time (ODT) is introduced as a method to determine the optimal scheduling strategy for destination-oriented elevators with reservation calls. The ODT is defined as the total time required for an elevator to service a passenger call, from its current position to the call floor and then to the destination floor. Second, a DNN is designed and trained to approximate the desired optimal ODT-based cost function, thus serving as a predictive scheduling model. Furthermore, evolutionary computation techniques—including particle swarm optimization, genetic algorithm, and covariance matrix adaptation evolution strategy—are employed to optimize the DNN parameters, leading to improved scheduling decisions that reduce passenger waiting time. Finally, experimental results verify that the optimized DNN consistently outperforms both the traditional nearest car method and the ODT method across diverse passenger flow patterns, demonstrating its significant advantage in improving EGCS performance.</p>