【Objective】A system of stand-level growth models with diameter-class disaggregation was developed for Korean pine (Pinus koraiensis) plantations in Heilongjiang Province to optimize stand management strategies, providing model support for developing management schedules and enhancing forest quality.【Method】Based on the remeasurement data from 218 permanent plots in Korean pine plantations in Heilongjiang Province during 1980—2023, a model system consisting of models for mortality, dominant height, stand basal area and diameter-class disaggregation was constructed. The Weibull function was used to disaggregate the predictions over diameter classes. The parameters of the growth models were estimated using the Gauss-Newton method and seemingly unrelated regression. The method of moments was used to recover the diameter distribution parameters for the diameter-class disaggregation. To verify the applicability of the model system, the study used the differential evolution (DE) algorithm with a model system to perform stand-level management optimization to find the rotation length that maximized wood production in different site indices (11.2, 14.2, 16.0 m).【Result】The components of the dynamic growth model explained over 90% of the variation in the modelling data. The final critical errors for stand mortality and basal area model obtained using seemingly unrelated regression were 16.769% and 17.685%, respectively. When applying the method of moments for parameter estimation of the Weibull equation, the pass rate of the Kolmogorov-Smirnov test was 96.946%. When using the growth with diameter-class disaggregation for predicting stand volume, using an existing taper model, the critical error was 14.612%. The optimization results indicated that, for the three stands, the thinning is by 1-3 years later as site index improves by 2 m, with the final harvest age ranging from 72 to 75 years.【Conclusion】The growth model constructed in this study satisfies the basic assumptions of path-invariance, consistency, and causality during management simulations, thereby allowing reliable growth simulations. Integrating the model with the DE algorithm provides effective forest management prescriptions, offering useful advise for the management decisions of Korean pine plantations.