A self-calibrating model for Diesel engine simulations is presented. The overall model consists of a zero-dimensional direct injection stochastic reactor model (DI-SRM) for engine in-cylinder processes simulations and a package of optimization algorithms (OPAL) suitable for solving various optimization, automatization and search problems. In the DI-SRM, based on an extensive model parameters study, the mixing time history that affects the level of in-cylinder turbulence was selected as a main calibration parameter. As targets during calibration against the experimental data, in-cylinder pressure history and engine-out emissions, including nitrogen oxides and unburned hydrocarbons were chosen. The calibration task was solved using DI-SRM and OPAL working as an integrated tool. Within OPAL, genetic algorithms (GA) were used to determine model constants necessary for calibrating. Engine-out emissions in DI-SRM were calculated based on the reduced mechanism of n-heptane. The developed simulation method has been applied to simulate Diesel engine performance parameters at part load conditions. It has been found that with the presented approach it is possible to efficiently calibrate Diesel engine model in terms of integrated parameters such as in-cylinder pressure. Additionally, the rate of formation of engine-out emissions, such as nitrogen oxides and unburned hydrocarbons can be well captured. Due to automatic determination of model parameters, the overall time required for complete engine model calibration decreases significantly. In the same time the accuracy of calibration increases when compared to manual calibration. The presented coupled simulations can be integrated within a global simulation method, which includes also engine performance optimization and tests of different fuels under Diesel conditions.