The efficient scheduling of employees is a critical aspect of workforce management in organizations, as it directly impacts productivity, employee satisfaction, and operational costs. However, the process of creating optimal employee schedules is inherently complex and often subject to various constraints, such as labor laws, shift preferences, and skill requirements. This project aims to optimize the employee scheduling system using the innovative Firefly Algorithm, a metaheuristic optimization technique inspired by the bioluminescent behavior of fireflies. The Firefly Algorithm is known for its ability to efficiently search for optimal solutions in large and complex problem spaces. Leveraging this algorithm, we propose a novel approach to handle the complexity of employee scheduling while ensuring fairness, efficiency, and employee well-being. The algorithm utilizes the concept of firefly attraction and movement to iteratively optimize the schedule, allowing the system to explore a diverse range of solutions and converge towards the most optimal one. The project's methodology involves developing a customized Firefly Algorithm implementation tailored to address the specific challenges of employee scheduling. We will design an objective function that considers multiple factors, including employee availability, skillsets, and shift preferences, while adhering to legal and contractual requirements. Furthermore, we will integrate user-friendly interfaces to facilitate input of scheduling constraints and visualization of the resulting schedules, enhancing the system's practicality and usability.