Aiming to reduce conservatism of the optimal solution with regard to robust optimization model, a light robust optimization approach was proposed to solve the open vehicle routing problem with travel time uncertainty. This approach yields routes that minimize the weighted sum of the number of defaulted vehicles and the total penalty cost. For each realizations of the uncertain data set, the optimal solution of the approach can ensure that its optimal value never exceeds a certain value, thus improving the conservatism of the optimal solution. To improve the probability of finding the optimal solution, the automatic design of genetic algorithms was proposed to solve the model. Its main idea is to use the particle swarm optimization algorithm to search components of genetic algorithm which can expectedly enable the genetic algorithm to generate the optimal solution and then to combine these components to generate a new genetic algorithm to solve the model. The new genetic algorithm was used to solve the model continuously and give rise to a new optimal solution. Calculation results show that the conservatism of the optimal solution solved by the proposed light robust optimization approach is significantly reduced comparing with the past robust optimization method.