Classical knowledge discovery tasks, such as classification, feature selection, association rules mining may be seen as multi-objective combinatorial optimization problems. Indeed, in many cases, some elements have to be combined to produce the solution that may be evaluated thanks to several quality criteria (it is usually necessary to maximize the specificity of the extracted knowledge while maximizing its generality to be applicable). Hence efficient multi-objective optimization techniques may contribute to extract interesting knowledge from datasets. In a context of big data, some additional specificities have to be taken into account, and metaheuristics are well suited to address them. In this presentation, I will focus on how knowledge discovery tasks may be modelled as multi-objective optimization problems and give some insight on how to solve them. I will also focus on the use of optimisation and multi-objective optimisation to realise the knowledge discovery pipeline.