Because the amount of knowledge in the engineering domain nowadays is huge, while being opaque, heterogeneous, distributed and informal at the same time, it is incredibly time-consuming for an engineer to find the knowledge (s)he is looking for and to reuse that knowledge. Furthermore, the amount of knowledge is growing exponentially, while the amount of qualified engineers is decreasing. To increase resource effectiveness and enable engineers to focus on what they do best (come up with creative design solutions) these time-consuming and non-creative tasks need to be removed from the engineer’s plate. This study set out to investigate and offer solutions to the issue of knowledge reuse relating to the knowledge contained in engineering rules, in context of a Knowledge-Based Engineering (KBE) based Multi-Disciplinary Optimization (MDO) framework, called the Integrated Design and Engineering Engine (IDEE). This research has investigated the different rule types that are required for capturing engineering rules. It was shown that these can be captured by a production rule language. To formally capture this knowledge, this research has identified the available technologies and based on this, extended the ontology based Knowledge Representation Model (KRM) of the IDEE with the production rule language RIF-PRD. Engineering rules are integrated into the KRM by means of a rule ontology that embeds the RIF-PRD expression. It was found that expressing formulas would benefit from an additional extension, which was added in the form of the mathematical language Content MathML. Formulas expressed in Content MathML are embedded into RIF-PRD. To increase the effectiveness of the IDEE approach, an implementation has been created by the author that provides the means to capture and reuse engineering rules in a more user-friendly way; from the point of informal capture to the generation of KBE application code usable by a KBE system, and the generation of process workflows usable by Simulation Work Flow Management (SWFM) tools. In addition, the implementation incorporates a reasoning engine in the form of elREy. This provides the IDEE with reasoning capabilities, used for instance to instantiate workflows. From the use cases it became apparent that all identified engineering rules were able to be captured by the extended KRM via the implementation. The implementation facilitated the modeling considerable. Because of the guidance the user gets, it is even for beginning rule modelers impossible to make a syntax error. For experienced programmers, the semi-graphical modeling might go somewhat slower, though, also due to annotation steps, but the value this brings in return is huge. The annotation provides so much information regarding origin, author, explanations, etc. that it makes the rules and the rationale easy to understand, even long after the creation of the knowledge. Because rules are stored separately they can be managed easily and directly linked to. Due to the capture of the semantics the knowledge contained in rules is easy to find, update and reuse, saving time (and money). Moreover, non-creative tasks carried out in order to reuse knowledge can be automated (e.g. generation of a KBE application). In addition, rules are platform independent and centrally stored, making sure that the knowledge they contain will not be lost and one consistent model is used in the entire framework.