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The mixed-cell cellular automata (MCCA) model improves the traditional cellular automata (CA) model, and introduces mixed cells based on the actual complex land structure, realizing the progress from qualitative and static simulation to quantitative and dynamic simulation. The applicability of the MCCA model in the Gan-Lin-Gao area (Ganzhou District, Linze County and Gaotai County) in the middle reaches of the Heihe River was firstly explored;after that, the multiple-objective programming (MOP) model and the ordinary linear regression model were separately used to predict the area values of different land use types in the sustainable development (SUD) scenario and the basic development (BAD) scenario in 2035, and then the area number was input into the MCCA model to visualize the land use spatial structure of different scenarios, and carry out comparative studies. The results showed that all accuracy evaluation indicators indicated that the simulation accuracy of the MCCA model was relatively high. The Kappa coefficient, mixed-cell figure of merit (mcFoM) and mean relative entropy (RE) were 0.886, 0.261 and 0.508, respectively, which was better than the patch-generating land use simulation model (PLUS) based on pure cells, so the MCCA model was suitable for the simulation of land use structure in the study area. In 2035, the scope of forest land in the SUD scenario was significantly higher than that in the BAD scenario, and its ecological benefits increased faster than that of the BAD scenario, construction land and arable land expand moderately, and the comprehensive benefits increased relatively fast. The results showed that the optimal land use allocation scheme simulated by coupling the MOP and MCCA model can better coordinate the relationship between economy and environment, which was not only conducive to rapid economic development, but also protected the ecological environment and maintains social stability.