Optimization modeling is popular for evaluating the design of energy systems in a given urban area. This is largely because the design of urban energy systems requires to make complex decisions about the choice of technologies, their location, and the fuels they use. This study presents an approach for modeling and optimizing decisions for retrofitting urban energy systems, with a focus on the optimal configuration and operation of supply side and demand side technologies required to satisfy the energy requirements. A mixed integer nonlinear programming model is formulated in GAMS and solved using Lindo optimizer. A case study in urban China is presented to verify the model and to identify opportunities for systems integration. Three scenarios are analyzed and the results show that a potential reduction in space heating and CO2 emissions of up to 57.7% and 50% are possible by retrofitting building envelopes with photovoltaics, ground source heat pump and natural gas cogeneration systems. Sensitivity analysis and multi-objective optimization further indicate that CO2 emission plays the most important role in decision-making. This approach enables to identify design trade-offs of complex urban energy systems so as to evaluate the potential of alternative technology mix.