Ionic liquids (ILs) have gained great interest recently to substitute volatile organic compounds (VOCs), since their properties can be tuned to match certain targets and applications. Further to this, another possibility to optimise ILs for their specific application is through IL mixtures. In this work, an insightful and yet simple systematic approach to design pure ILs and their mixtures is presented. This newly presented approach allows the visualisation of IL mixture design problem, and hence provides insights and allows users to solve the problem visually. The visualisation of problem and solutions is achieved by applying property integration framework in this proposed methodology. In property integration framework, IL products design problem is mapped from property domain into cluster domain through property clustering technique. Therefore, the proposed methodology provides a property based platform to visualise the overall performance of the designed IL products with graphical tools. A feasible IL product is always designed to fit a purpose based on consideration of multiple target properties, but these properties can be contradicting one another. The presented approach allows multiple target properties consideration during the design process, by portraying these properties and target of each clearly on a single graphical tool. To date, the study of properties of pure ILs and IL mixtures is still in the infant phase, and these data are still scarce. Hence, some of the prediction models do not cover all available ILs. To overcome this problem, the proposed approach is developed to adapt property data of pure ILs directly, together with existing property prediction models to predict the properties of the designed IL mixtures. The presented approach is able to generate a list of potential solutions to users, and the final decision can be made by users accordingly, through further screening and experimental validations. An illustrative case study, which focuses on the design of carbon capture solvents, is solved to demonstrate the proposed approach. (C) 2016 Elsevier Ltd. All rights reserved.