This thesis is about Open selective vehicle routing problem with pricing.This problem considers maximizing profit when customer demands reduce as we increase the price. The demand reduction affects the objective function profit and thus we may not serve some customers. Also, due to the assumption of using rental vehicles for traortation, vehicles are not required to return to the depot.On the other hand, in order to maximize the satisfaction of distributors, even distribution of goods between distributors is the secondary objectivefunction. Two mathematical models are proposed. The first model is a single-objective model which considers sales revenue minus distribution coststo maximize the profit with respect topricing. The second model is a bi-objective model.The first objective issales revenue minus cost of goods distribution and the second objective considers load balancein order to maximize distributors’ satisfaction. One metaheuristics named Improved Simulated Annealing(ISA)algorithm is proposed to solve single-objective model. For validating this method, some small scale problems are solved and results are compared to the results of an exact method and Simulated Annealing algorithm. The comparison of results shows that theproposed method issuitable for solving the model. For investigating its efficiency in dealing with real world problems, somelarge scale problems are solved and the results are compared tothe results ofSimulated Annealing (SA)algorithm. Results show that ISA and is more efficient than SA. One metaheuristic named Multi Objective Imperialist Competitive Algorithm (MOICA) is implemented to solve the bi-objective model. Some small scale problems are solved to examine its validation using an exact method. The computational results indicate efficiency of this method. Also some large scale problems are solved to show its efficiency in solving real world problems. The results are compared tothe results of Non-dominated Sorting Genetic Algorithm-II (NSGA-II). It isshown that MOICA outperforms NSGA-II.