DSS have been enriched with techniques derived from Artificial Intelligence (AI), in particular the development of an intelligent Decision Support System (IDSS), so as to give the DSS the ability to provide intelligent support from domain knowledge, models and problem-solving strategies. An IDSS emulates a decision maker, reasons, solves problems and analyzes results. This paper focusses on the design and development of a Hybrid Intelligent Decision Support System (HIDSS) for the equipment maintenance in industrial installations to promote a better decision and enhance maintenance efficiency. HIDSS is based on a hybrid knowledge representation and reasoning structure that integrates Case-Based Reasoning (CBR), Ontology and Genetic Algorithms (GA). With this integration, HIDSS not only performs data matching, but also performs semantic access to associated knowledge, which is important for an intelligent retrieval of knowledge in decision support systems. The execution of an industrial equipment maintenance case illustrates the use of the proposed HIDSS and shows the applicability and the feasibility of our approach as well as the benefit of the combination of CBR, GA and Ontologies technologies for knowledge-intensive decision support systems.