Increasing the level of autonomy facilitates a vehicle in performing long-range operations with minimum supervision. This paper shows that the ability of Autonomous Underwater Vehicles (AUVs) to fulfill mission objectives is directly influenced by route planning and task assignment system performance. This paper proposes an efficient task-assign route-planning model in a semi-dynamic network, where the location of some waypoints can change over time within a target area. Two popular meta-heuristic algorithms, biogeography-based optimization (BBO) and particle swarm optimization (PSO), are adapted to provide real-time optimal solutions for task sequence selection and mission time management. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of Monte Carlo simulation trials are undertaken. The results of simulations demonstrate that the proposed methods are reliable and robust, particularly in dealing with uncertainties and changes in the operations network topology. As a result, they can significantly enhance the level of vehicle's autonomy, enhancing its reactive nature through its capacity to provide fast feasible solutions.