A novel multidisciplinary design method is developed in this research to optimally evolve and synthesize multi-domain engineering systems. Using an inductive genetic programming (IGP) along with a bond graph modeling tool (BGIGP), the proposed design algorithm dynamically explores the design space for finding the structure of optimal design solutions. The design methodology utilizes two navigated optimization loops to handle both topology and parameter optimization. In the outer loop, an IGP tool is applied on an embryo bond graph model of the system for topology synthesis. In the inner loop, an optimization tool that incorporates an artificial immune system (AIS) is utilized for parameter value optimization. A supervisory loop statistically analyzes the effectiveness of different engineering elements in enhancing the system performance. By acquiring knowledge and learning from prior trials, the evolution parameters are automatically and intelligently tuned to make the design model more reliable, and to provide more effective navigation for the solution in comparison with prior work. The developed method is compared with an available method of bond graph-genetic programming (BGGP) for designing an engine mount system. In another proposed design methodology, bond graph modeling along with artificial immune system (BGAIS) tool are utilized. The design methodology utilizes two navigated optimization loops to handle the optimization of both topology and parameters. In the topology optimization, which is the outer loop, the proposed artificial immune system (AIS) tool is utilized, while in in the inner loop, an optimization tool that incorporates simple AIS is used for parameter tuning in any generated topology. A novel concept of “artificial vaccination” is developed, which is responsible for the incorporation of domain knowledge. Numerical studies on the design of a machining vibration controller, hydraulic engine mount, and suspension system of vibratory equipment are presented to investigate different specification of the proposed methodology. In the case of distributed system design, optimal design of a cantilever piezoelectric energy harvester is presented, with the aim to capture maximum electrical power from a vibratory feeder in mining industry. Innovatively, intelligent artificial immune system (AIS) is utilized for multi-objective optimization of the shape parameters of the system. To verify the presented analytical shape optimization method, finite element analyses, as well as experimental investigation are also performed. Keywords: Multidisciplinary design, Inductive genetic programming, Bond graph, Artificial immune system, piezoceramic energy harvester, Optimization.