<p>Planning for sustainable development involves balancing a number of competing priorities and objectives that are inextricably linked, such as economic development, employment generation, use of energy and environmental protection. These are interdependent, and it is necessary to have optimization methods that can consider trade-offs between various components of the system in order to plan effectively. In this study, an integrated goal programming (GP) framework is introduced, which is based on weighted goal programming and fuzzy goal programming and is optimized in a single model. The weighted component allows the achievement of the set targets based on their relative weight, and the fuzzy component allows for the partial fulfilment of the established goals, thereby dealing with the uncertainty. A major shortcoming of traditional fuzzy goal programming is that when the performance of one goal is good, the performance of the other goal may be poor, so the proposed framework adds a penalty term, which clearly penalizes low performance of the individual goals. The model is designed to give a balanced and practically meaningful solution for policy formulation because it simultaneously rewards higher level of goal satisfaction and penalizes underperformance. The effectiveness of the proposed approach is shown with a numerical example, covering significant dimensions of sustainable development such as economic performance, employment, energy use and environmental impact. The findings revealed that the framework can provide a more balanced compromise between the competing objectives than conventional methods and that it leads to a better outcome on key sustainability indicators, while causing minimal negative impact on the other indicators. The proposed framework is flexible and generally applicable, and hence it is a valuable decision support methodology for integrated sustainable development planning as well as a contribution to the field of multi-objective optimization and process integration in general.</p>