Unlike traditional measurements by diffusion tensor imaging, multidimensional diffusion MRI allows the estimation of additional microstructural parameters such as anisotropy, kurtosis and orientation dispersion. To properly take advantage of this imaging modality and capturing microstructure parameters accurately and efficiently, it is crucial to use a dedicated acquisition scheme. Several models and acquisition representations can be used towards this goal. In this paper, we focused on the q-space trajectory imaging, using b-tensor acquisition encoding and the diffusion tensor distribution (DTD) modeling. More specifically, our goal is to develop a framework for the optimization of acquisition scheme based on their ability to properly estimate microstructural parameters of interest. We generated an extensive collection of b-tensor shapes with a fixed number of directions each, from which we efficiently selected an optimized acquisition scheme. In the spirit of fingerprinting, we proposed a dictionary-based approach. The dictionary columns were carefully adapted to the achievable resolution in the parameter space, the parameters of interest being the microscopic anisotropy, the tensor size variance and the orientation parameter. To solve the combinatorial optimization problem of selecting the best subset of b-tensor shapes, we implemented two approximation algorithms: a greedy approach based and a permutation strategy. To assess the performance of our optimization procedure, we computed the estimation error for each parameter. The signal generated from our scheme yielded lower or comparable errors to those of a reference scheme proposed in the literature and designed for this purpose.