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Energy consumption in commercial buildings has been growing substantially in recent years. Recently, building energy consumption estimation tools have been used to calculate energy savings and emissions reduction. Energy performance of building is complicated since it depends on multiple variables associated to building characteristics, equipment and systems, weather, occupants, and sociological influences. Therefore, the objective of this study is to develop the multi-linear regression models to predict energy consumption of an office building in five different climate regions in the United States. In order to achieve this objective, a typical commercial building was selected and the effect of 17 key building design parameters on its energy performance was investigated. To quantify building energy consumption, eQuest and DOE-2, which are building energy simulation software programs, were used to develop the building profile and perform annual energy simulation. In addition, Monte Carlo simulation technique was used to create a ten thousands comprehensive dataset covering the full range of design parameters for each studied climate region. An in-house computer program was developed to implement the Monte Carlo simulation. Statistical analysis was performed using R statistical analysis program to develop a set of linear regression equations predicting energy consumption of each design scenario. The difference between obtained results from regression model and DOE-2 are largely within 5%. In addition, standardized regression coefficient was calculated to assess the sensitivity of heating, cooling, and total energy loads to different building design parameters across five climate zones. It is believed that the developed regression models can be used to estimate the energy consumption of office buildings in different climate regions when designers and engineers consider various building envelope designs in the early stages of the design.