The inversion method combining the genetic neural network and the discrete element simulation of triaxial tests is firstly described for determining the discrete element model parameters of the conditioned soil. The purpose is to make the error of the simulation curves and the laboratory curves of the triaxial test minimum. The solve approach is the parameters identification based on the genetic neural network. The network training sample is provided by the discrete element simulation. The input sample is the simulation curves of triaxial test, and the output sample is the model parameters. The laboratory triaxial test curves of the conditioned soil are used to determine its model parameters. The simulation curves calculated with the inversed parameters match the laboratory curves well, which illustrate that the discrete element model can accurately predict the deformation characteristics and flow patterns of conditioned soils.