The efficiency of the SAGD process depends on two important factors: reservoir properties and operating conditions. SAGD performance was investigated based on the variables of reservoir properties such as thickness, porosity, permeability, oil saturation, viscosity, rock thermal conductivity, along with operating variables as including preheating, injector/producer spacing, injection pressure, steam injection rate and subcool temperature. In addition, the economic risks associate to the high capital and operation expenditures, and uncertainties of oil and gas prices in the market. In order to manage the uncertainties of oilsands project, we need the quantitative analysis of concerned parameters affecting returns. Then, we can propose optimization design for operating conditions.The previous studies conducted sensitivity analysis and optimization of SAGD performance by classical methods. Therefore, there was a lack of confidence level because they did not determine the significance level of parameters and ignored interactions effects between considered parameters, lead to low efficiency issues in a field operation. Furthermore, the economic models were not comprehensive enough with limited consideration on few factors. These restrictions of classical method can be avoided by applying D-optimal design and response surface methodology to find the best regression model for SAGD performance.There were a total of 75 cases for screening reservoir and operational parameters with the NPV responses based on the D-optimal design. The results showed that reservoir properties have a greatest influence on the SAGD performance with ranking order of porosity, thickness, oil saturation, permeability, viscosity, respectively. The optimization design of operating conditions obtained the maximum NPV when vertical well spacing 9m, injection pressure 5,000kPa.