Traditionally, the design of new chemical products for specific applications is done by using a combination of design heuristics, experimental studies and expert judgements. In addition to the conventional methods, chemical products can also be designed by using computer-aided molecular design (CAMD) techniques. Based on CAMD, optimal chemical products can be designed by identifying the molecule with the best properties that correspond with the target functionalities of the product. In general, the optimality of product property (termed as property superiority) is the only factor considered while designing optimal products by using CAMD techniques. However, it is noted that property prediction models are developed with certain accuracy and uncertainties. As the accuracy of property prediction models (termed as property robustness) can affect the effectiveness of CAMD techniques in predicting the product property, the effects of property prediction uncertainty have to be considered while applying CAMD techniques. This paper presents a systematic fuzzy optimisation based molecular design methodology. The methodology is developed for the design of optimum molecules used in chemical processes by considering and optimising both property superiority and robustness. Property superiority is quantified by property optimality. Meanwhile, property robustness is expressed by the standard deviation of the property prediction model, which is a measure of average variation between the experimental data and estimated values of product property using property prediction model. Fuzzy optimisation approach is extended in this work to address and trade off property superiority and robustness simultaneously. Molecular design technique is adapted in this work to identify the optimal molecular structure which satisfies multiple product specification. To illustrate the proposed method, a case study is presented where optimal solution is selected based on how much the solution satisfied the criteria of property superiority and robustness. (C) 2015 Elsevier Ltd. All rights reserved.