Abstract: China is in the stage of rapid development of urbanization. However, policy makers often face such problems in the urbanization process: What is the maximal population scale that the resources of a city can support? How to adjust the industrial structure in order to maximize the population carrying capacity? What is the reasonable setting value of gross domestic product (GDP) growth for the next year? What is the quantitative relationship between GDP growth and consumer price index (CPI), employment? Which areas should be invested in by financial investment in order to maximize resident satisfaction? What decisions affect the coordinated development of a city? And so on. How to meet these problems in the context of urbanization, and how to make good top-level plannings and designs of urban development are major and complex problems. For such problems, by using system science and intelligent methods combing qualitative and quantitative analysis, we first construct an intelligent forecasting model of key indexes of urban development such as GDP growth and government revenue, also we investigate a quantitative relationship model between GDP growth and CPI, Employment. Then we construct the optimization model for improving the urban population carrying capacity and the coordinated development of the city. We also make an extensive case study for some important decision scenarios of a large city. The results show that the proposed method is a novel and effective one which can be used to study and solve such problems, and that the experimental results may contribute to improving the decision making level of urban policy-makers, achieving scientific decision-making qualitatively and quantitatively.