We study hierarchical properties of optimal transportation networks with biological background. The networks are obtained as minimizers of an energy functional which involves a metabolic cost term of a power-law form with exponent \(\gamma >0\) . In the range \(\gamma \in (0,1)\) , most relevant for biological applications, the functional is non-convex and its local minima correspond to loop-free graphs (trees). We propose a numerical scheme that performs energy descent by searching the discrete set of local minimizers, combined with a Monte-Carlo approach. We verify the performance of the scheme in the borderline case \(\gamma =1\) , where the functional is convex. For a particular example of a leaf-shaped planar graph, we evaluate the global reaching centrality (GRC) of the (local) minimizers in dependence on the value of \(\gamma \in (0,1]\) . We observe that the GRC, which can be understood as a measure of hierarchical organization of the graph, monotonically increases with increasing \(\gamma \) . To our best knowledge, this is the first quantification of the influence of the value of the metabolic exponent on the hierarchical organization of the (almost) optimal transportation network.