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The approach of choosing an effective low-carbon land-use structure by multi-objective methods is commonly used in land-use planning. A common methodology is to calculate carbon emissions and conduct scenario simulations for the future. However, most Chinese cities have not implemented the methods for monitoring carbon emissions proposed by the Intergovernmental Panel on Climate Change (IPCC), especially Shenzhen, which is one of the fastest-growing cities in China. This study first calculated the carbon emissions for a typical year in Shenzhen under the guidance of the IPCC. Second, nighttime light data were used to spatialize the gross domestic product to obtain the economic benefit coefficients of the various land types. Finally, a multi-objective linear programming model was used to optimize the land-use structure under different scenarios for 2020 and 2025. The results show that (i) energy consumption contributed the most to local carbon emissions in 2016, at 94.75%; (ii) carbon emissions from paddy fields, animals, and humans were the second most dominant source; (iii) the intensity of carbon emissions from different land types in 2016 was variable; and (iv) compared with the natural scenario, an optimized land-use structure could reduce carbon emissions by 5.97% by 2020 and 12.61% by 2025. Under ideal simulation conditions, the simulated land-use pattern could not only meet the requirements of economic and social development, but also could effectively reduce carbon emissions, which is of great value to land managers and decision-makers.