This paper presents a goal programming algorithm utilising a weight-free aggregate function for producing enhanced design alternatives and a knowledge-based procedure for the selection of the final solution from a pool of enhanced alternatives. Normally, in a multi-disciplinary design problem several teams of designers with different preference and background knowledge are involved in the decision making processes, such as constructing aggregate functions for multi-objective optimisation and trade-off study towards selecting the final solution. In constructing an aggregate function, designers need to identify how important each objective is with respect to the other objectives. However, in the absence of a final decision maker with expertise in all disciplines, the predicates such as “as important as” or “more important than” cannot be used to compare objectives from different disciplines, and therefore the establishment of a weighted aggregate function is not viable. Introducing the concepts of unsatisfactoriness and tolerated margin, “how important is a design quality with respect to other design qualities” is replaced with “to what extent can the unsatisfactoriness of a design quality be tolerated”. This removes the predicament arising from the usual subjective decision making when forming an aggregate function and also transforms the final solution selection from a negotiation process to a straightforward and knowledge based procedure. A software tool comprising of two modules, a multi-deme genetic algorithm, for producing enhanced alternatives, and an assessment module, which includes visualisation, ranking and filtering facilities, is developed and its performance is shown using an illustrative multi-disciplinary design space.