Early in the design process, designers attempt to explore in the wide design space to generate a number of feasible solutions and decide the best concept scheme with a high degree of uncertainty according to customer demands. A strategy that can assist designers in exploring and ascertaining design solutions within this vast space is therefore crucial. However, existing product design tools mainly focus on the detailed design phase and due to lack of effective design tools, it is often difficult for human designers to explore in wide multi-disciplinary solution spaces. Therefore, this paper proposes a cognitive process-based approach for assisting designers achieving intelligent conceptual design. By analyzing designers’ thinking characteristics, a design meta-thinking model is defined and a simulation model which is composed of divergent thinking and convergent thinking is put forward. Case-based reasoning and genetic algorithm are applied to stimulate divergent thinking to associate and produce feasible concept solutions. Furthermore, multiple-attribute decision-making method based on intuitionistic fuzzy number is employed to stimulate convergent thinking to obtain the best solution from generated conceptual candidate solutions. Finally, a case study is implemented on a turbo-expander. The result of this example demonstrates that the proposed approach can provide an intelligent manner to perform conceptual design. Meanwhile, a computer-aided design prototype system is developed based on this framework.