Rheometers with specific impellers, developed to characterize the behavior of cement-based suspensions in the fresh state, are used to limit the heterogeneities induced during shearing but they make the identification of the rheological parameters less straightforward compared to conventional rheometers. This paper presents the inverse analysis method and discusses the quality of this identification procedure when applied to such materials. The procedure includes a CFD simulation based on the finite element method using a Herschel-Bulkley model. Two kinds of optimization algorithms are used: a deterministic simplex method and a stochastic genetic method. As a first step of a larger study, the procedure reported in this paper used 2D synthetic data, i.e. 2D numerically generated experimental data. Two numerical vis-coplastic materials, characterized by shear-thinning and shear-thickening, were selected and studied. The results obtained with the two algorithms are systematically compared to the known parameter solution. Three approaches corresponding to three levels of user's knowledge about the material under study are considered successively: (1) the user has no a priori knowledge about the material, (2) the user knows whether the material is shear-thinning or shear-thickening and (3) the user is able to estimate the behavior index. The time-consuming genetic method appears to be suitable when the a priori knowledge of the material is slight, whereas the simplex method gives a reliable solution in a few iterations when the level of knowledge is higher. Both algorithms encounter more difficulties with the shear-thinning material than with the shear-thickening one. In this paper, the advantage provided by the genetic method regarding the non-uniqueness of the identification procedure for real experimental data is also highlighted: this method provides a collection of satisfactory solutions among which the user can select the optimal one based on his scientific background and/or on further experimental test results. (C) 2017 Elsevier B.V. All rights reserved.