In this article an improvement to a current methodology for testing iterative reconstruction methods is presented. This improvement consists in the use of a multidimensional global optimization algorithm for the best estimate of the free parameters of the reconstruction process. This algorithm is based on a probabilistic random search method. The global optimization algorithms allow us to achieve the best performance of the reconstruction algorithms in order to compare them under the optimal conditions. This methodology has been applied for the testing of a MART (Multiplicative Algebraic Reconstruction Technique) algorithm performance using the classical cubic-shaped voxels and spherical symmetric basis functions, also known as blobs, as the basis functions for the image representation in 3D X-ray cone-beam transmission tomography.