Traditionally, scheduling problems usually deal with the objectives related to production efficiency (e.g., the make-span, the total completion time, the maximum lateness and the number of tardy jobs). However, sustainable manufacturing should minimise the energy consumption during production process. Energy consumption not only constitutes a major portion of total production cost but also results in significant environmental effects. In this chapter, we discuss a multi-objective scheduling problem in a hybrid flow shop. Two objectives considered in the proposed model are to minimise make-span and energy consumption. These two objectives are often in conflict with each other. A Preference Vector Ant Colony System (PVACS) is developed to search for a set of Pareto-optimal solutions using meta-heuristics for multi-objective optimisation. PVACS allows the search in the solution space to focus on the specific areas which are of particular interest to decision-makers, instead of searching for the entire Pareto frontier. This is achieved by maintaining a separate pheromone matrix for each objective, respectively and assigning each ant a preference vector that represents the preference between the two objectives of the decision-makers. The performance of PVACS was compared to two well-known multi-objective genetic algorithms: SPEA2 and NSGA-II. The experimental results show that PVACS outperforms the other two algorithms.