Tensor-valued diffusion encoding has allowed for increased specificity in diffusion MRI, probing the diffusion patterns of water molecules in vivo along new dimensions. From an intuitive standpoint, a versatile sampling scheme should be sensitive to a diverse set of diffusion profiles in any given voxel. However, while optimization strategies based on electrostatic repulsion achieve this for conventional diffusion sampling scheme, no equivalent optimization exists for tensor-valued diffusion data. In this work, we derive an optimization strategy based on maximizing the Frobenius distance between b-tensors. Its evaluation in silico demonstrates that it increases the accuracy of diffusion tensor distribution imaging.