This paper presents an improved Distributed Genetic Algorithm (DGA) that surpasses the classic genetic algorithm (GA) in terms of both efficiency and effectiveness when it comes to optimizing faculty scheduling systems. The proposed Differential Evolution Genetic Algorithm (DGA) combines DE with chaotic mapping and asynchronous communication to effectively preserve genetic diversity and accelerate convergence towards optimal solutions. The results demonstrate a significant decrease of over in computational time, while simultaneously enhancing the quality of the solution. The findings highlight the potential of the suggested model to be applied in real-world situations, providing a fast and strong scheduling tool for academic institutions. The study suggests that further research should be conducted to determine if the method can be applied to new areas. It also highlights the need to consider the trade-off between speed and accuracy in various computational situations. [For the complete proceedings, see ED672804.]