The Intelligent Water Drop (IWD) algorithm is inspired by the movement of real water drops in a river. A water drop could find an optimum path to a lake or sea by interacting with the conditions of its surroundings. In the process of reaching such destination, the water drops interact with the river bed while they move through it. Similarly, the supply chain problem can be modelled as a flow of supply, manufacturing, and delivery stages that must be completed to produce a finished product and then to deliver it to the end user. The problem is to select one option that carries out the stage, e.g. for a supply stage, many suppliers could supply the component represented by it. As each stage is characterised by its time and cost, multi--objective optimisation algorithm is used to minimise the time to market and production cost, simultaneously. Focusing on this analogy, this paper proposes an approach to the supply chain problem using a multi--objective extension to the intelligent water drops algorithm. Artificial water drops, flowing through the supply chain, will simultaneously minimise the production cost and the time to market of every product in a generic BOM by using the concept of Pareto optimality. A widely-used notebook supply chain in literature is solved. We provide some performance metrics of the solution and compare the Pareto set computed by the proposed algorithm with the one returned by exhaustive enumeration.