Trust-aware recommender system (TARS) recommends ratings based on user trust. It greatly improves the conventional collaborative filtering by providing reliable recommendations when dealing with the data sparseness problem. One basic research issue of TARS is to improve the recommending efficiency, in which the key point is to find sufficient number of recommenders efficiently for active users. Existing works searched recommenders via a skeleton, which consists of a number of hub nodes. The hub nodes are those who have superior degrees based on the scale-freeness of the trust network. However, existing works did not consider the skeleton maintenance cost and the coverage overlap between nodes of the skeleton. They also failed to suggest the proper size of the skeleton. This paper proposes an optimized TARS model to solve the problems of existing works. By using the genetic algorithm, our model chooses the most suitable nodes for the skeleton of recommender searching. It can achieve the maximum prediction coverage with the minimum skeleton maintenance cost. Simulation results show that compared with existing works, our model can reduce more than 90{\%} of the skeleton maintenance cost with reasonable prediction coverage.