EV fleets in the EU/US region are owned by a fleet manager who manages the entire fleet operation. Currently, the telematics ECU transmits a significant amount of information that can be used to our advantage for vehicle–route assignment. The beneficiaries of this solution would be the fleet managers, who will receive an optimized vehicle–route plan, and the drivers, who will be able to see their entire week’s schedule. This can be achieved using models and applications in operations research, which take in EV vehicle parameters and provide an optimal solution. Existing fleet and vehicle control strategies typically rely on fixed actuation limits or rule-based logic that are configured without direct awareness of fleet-level objectives or task-specific requirements. As a result, vehicles may operate inefficiently, experience unnecessary energy consumption, or incur accelerated component aging. There is therefore a need for a system in which fleet and task information directly influence vehicle actuation in a dynamic and coordinated manner. This approach enables fleet operators to reduce energy cost, battery degradation, and unplanned downtime by dynamically aligning vehicle actuation with actual task and fleet needs rather than static calibrations. It improves fleet utilization and scalability by allowing vehicles to automatically adapt their energy and power behavior as operational conditions change. As electrified and mixed fleets grow and complexity increases, such optimization-driven actuation becomes critical for achieving cost-efficient, reliable, and sustainable fleet operations. The scheduling is modelled in three distinct layers that is vehicle-task scheduler, charging scheduler, human driver scheduler, thereby ensuring a clean modular design.