Résumé: The use of finite element calculations to deal with geotechnical problems is therefore limited by poor knowledge of the mechanical parameters of soils. The identification of these parameters characterizing the soil behavior model involves solving the inverse analysis problem. This form of inverse analysis consists in calibrating a numerical soil model on experimental data by iterative modifications of the values of the input parameters of the model until the difference between the result of the numerical calculation and the experimental data is minimal. In this article, we study the use of the principle of inverse analysis for the identification of the parameters of the constitutive soil model Mohr–Coulomb: the shear modulus (G) and the friction angle (φ). The inverse analysis problem posed by the determination of the parameters of the model is solved using an optimization technique based on two stochastic optimization algorithms, the genetic algorithm and the hybrid genetic algorithm with the tabu search method. These two optimization methods have been validated on a pressuremeter test. The results obtained by applying the genetic algorithm method and the hybrid genetic algorithm method for the identification of the two Mohr–Coulomb parameters (G and φ) show that the hybridization process of the genetic algorithm with the tabu search method accelerated the convergence of the algorithm towards the exact solution of the problem whereas the genetic algorithm alone takes a much longer computation time to reach an optimum close to the exact solution of the problem.