The objective of this project was to evaluate the ability of the CERES-Maize crop growth model to simulate grain yield response to plant density and N rate for two soil types in Northeast China, with the long-term goal of using the model to identify the optimum plant density and N fertilizer rate forspecific site-years. Nitrogen experiments with six N rates, three plant densities and two soil types were conducted from 2015 to 2017 in Lishu county, Jilin Province in Northeast China. The CERES-Maize model was calibrated for 2015 and 2016, and evaluated for 2017 experiments. Results indicated that the model provided good estimations of yield across plant densities and N rates for the calibration years (R2=0.83 for black soil and R2=0.89 for sandy soil) and evaluation year (R2=0.91 for black soil and R2=0.95 for sandy soil), respectively. The calibrated model was then run using weather data from 1965 to 2017 for 15 different N rates, and 7 different plant densities to determine the optimum N rate and plant density for two soil types in different weather conditions. Model analysis indicated thatthe optimum plant density for black soil was 5.1-6.6, 7.3-9.2, and 8.3-8.4×104ha-1for dry, normal and wet years,respectively. For sandy soil, the optimum plant density was 3.0-3.9, 4.5-7.3, and 5.2-6.7 ×104ha-1for dry, normal and wet years respectively. For the optimum N rate, the value was 183-209, 231-239, and 220-228kg ha-1for dry, normal and wet years, respectively, for black soil. The optimum N rate was 171-186, 216-224, and 229-285kg ha-1for sandy soil for dry, normal and wet years, respectively. We concluded that the CERES-Maize model was able to simulate maize growth and yield, and could be used as atool to assist precision plant density and N management for different soil types and weather conditions in Northeast China