Biodiesel is the most common and most investigated of the alternative diesel fuels due to it presents many important technical advantages compared to petro-diesel. The major difficulty to biodiesel commercialization is its high cost, which is approximately 1.5 times higher than petroleum diesel fuel, due to the cost of vegetable oil. Beside alternate catalysts and reaction schemes, other methods to reduce the production cost of biodiesel has included feedstock substitution. However, biodiesel from some feedstock like oil crops is not sustainable because of the increasing food price, decreased agriculture land, and depletion of soil. Therefore, biodiesel production is confronted with two main tasks: cost reduction and ecological restrictions. Also, beside these objectives, the social involvements of biodiesel production and use must not be neglected. The best decisions regarding all these aspects imply solving multiobjective optimization problems. As an example, a case study concerning with optimal economical (maximization of the net profit), environmental (minimization of VOC emissions) and social (maximization of the number of chemical operators' jobs) design of the biodiesel production from soybean oil is presented. This multiobjective optimization problem was solved by process simulation with SuperPro Designer and process optimization with a Matlab Genetic Algorithm. The link between Matlab and SuperPro Designer has been accomplished by a Matlab graphical user interface based on COM technology. From the obtained Pareto fronts, the optimal compromise solutions can be selected. (C) 2014 Elsevier Ltd. All rights reserved.