Buildings are responsible for 38% of all carbon emissions and 14% of water consumption in the United States. These negative environmental impacts can significantly be reduced by implementing green upgrade measures such as energy-efficient lighting and HVAC systems, motion sensors, photovoltaic systems, and water-saving plumbing fixtures. Building owners in the public and private sectors often search for an optimal set of upgrade measures that is capable of minimizing the negative environmental impacts of their buildings. This paper presents the development of an optimization model that is capable of identifying optimal selection of building upgrade measures to minimize greenhouse gas emission and water consumption of existing buildings while complying with limited upgrade budgets. The model is developed in four main development steps: metrics identification step that quantifies greenhouse gas emissions and water consumption of existing buildings; model formulation step that formulates the model decision variables, objective function, and constraints; implementation step that executes the model computations and specifies the model input and output data; and validation step that evaluates the model performance using a case study of an existing building. The results of the model illustrate its new and unique capabilities in providing detailed results, which include specifications for the recommended upgrade measures, their location in the building, and required upgrade cost to minimize greenhouse gas emissions and water consumption of existing buildings.Non UBCUnreviewedFacultyOthe