Hubs are facilities used to treat and dispatch resources in a transportation network. The objective of Hub Location Problems (HLP) is to locate a set of hubs in a network and route resources from origins to destinations such that the total cost of attending all demands is minimized. In this paper, we investigate a particular HLP, called the Tree of Hubs Location Problem in which hubs are connected by means of a tree and the overall network infrastructure relies on a spanning tree. This problem is particularly interesting when the total cost of building the hub backbone is high. We propose a biased random key genetic algorithm for solving the tree of hubs location problem. Computational results show that the proposed heuristic is robust and effective to this problem. The method was able to improve best known solutions of two benchmark instances used in the experiments.