The vehicle routing problem and product/service delivery scheduling are of the most important problems in logistic management. This research aim to present models for developing of the delivery time window setting based on routing models as one of the most important factors of logistic management. In the first section of this research, a comprehensive review of time window management criteria and delivery time utility functions in the routing problems is studied. Then the Vehicle Routing Problem with General Soft Time Window (VRPGSTW) is defined based on the new general flexible criterion for the time window management and a mathematical model is formulated. Also based on the column generation approach, this problem is decomposed in to the set covering master problem and the Elementary Shortest Path Problem with Resource Constraints and General Soft Time Window Cost (ERCGSTWC) subproblem. Then the complexity of this model is examined and a lower bound based on the decomposition approach is presented. In the following, a modified sweep-based heuristic, a Quantum-iired Evolutionary Algorithm (QIEA) and a novel hybrid column generation –metaheuristic are proposed to solve the VRPGSTW. The parameter of these algorithms have been tuned by full factorial design of the experiment and the efficiency of they are assessment on the modified benchmark problems. The results show that QIEA solve optimally the small-size problems and hybrid column generation –metaheuristic has 3.6% avearge gap of optimal solutions in medium and large-size problems. In the second section of this research, the Vehicle Routing Problem with Multiple Prioritized Time Window (VRPMPTW) is defined and a mathematical model is formulated. After examination of complexity of the proposed model, an efficient algorithm is proposed based on the co-evolutionary concept named as the Cooperative Coevolutionary Multi-Objective Quantum-Genetic Algorithm (CCMQGA). Also a new multi-objective local search (FPASLC) is proposed that create a well-distributed Pareto front. Finally the proposed algorithm is applied in the mentioned case study in a distribution company. The results demonstrate the efficiency of the proposed algorithm in comparing than NSGAII results and the solution that provided by experts of distribution company in which the proposed algorithm improve 30% of number of vehicles and customers satisfaction.