Employee transportation is an operational challenge for numerous industrial organizations seeking to enhance their overall productivity. This challenge is classified as an NP-hard problem, as it is an extended variant of the Vehicle Routing Problem (VRP), named Employee Bus Routing Problem (EBRP). In recent years, the EBRP has been extensively studied using various approaches, including heuristics, metaheuristics, and exact algorithms. However, efficiently solving large instances of the bi-objective EBRP remains a significant challenge for researchers. This paper examines a practical application of the EBRP for a large Moroccan industrial group that provides shuttle services for its employees across multiple regions. This company aims to operate a fleet of heterogeneous buses to ensure the comfort of thousands of employees while minimizing operational costs. Therefore, we propose a bi-colony Ant Colony Algorithm (BC-ACO) with two competing colonies: one focused on minimizing total cost and the other on reducing the maximum travel time for passengers. Each colony independently explores potential solutions while iteratively sharing information with one another throughout the optimization process. To evaluate the efficiency of the proposed approach, we conduct a comparative analysis against three other methods: a weighted sum bi-objective Genetic Algorithm (BGA), bi-objective ACO with one colony (BACO) and an exact approach. The results indicate that the BC-ACO yields solutions that provide a better trade-off and within a minimized execution time, making it a viable option for large-scale applications in the real world.