This paper investigates a real life bi-objective hybrid flow shop scheduling problem in an energy-intensive manufacturing system, in which glass is produced successively in cutting, printing and tempering stages. The problem aims to simultaneously optimize makespan and the total electricity cost under a time-of-use electricity pricing policy. The glass production has to respect the following environments: (i) the cutting and printing operations are processed in parallel machine environments; (ii) the tempering operation is processed on a batch machine; (iii) machine eligibility and setup time have to be considered in the cutting and printing stages; (iv) the whole manufacturing system is under a time-of-use electricity pricing policy. For the problem, an integer programming model is firstly proposed and shown to be strongly NP-hard. Then a model-based heuristic is adopted and a bi-objective differential evolution algorithm (BODE) is devised based on problem features. Computational experiments on randomly generated instances demonstrated that the BODE outperforms the model-based heuristic in terms of computation time and solution quality. Moreover, with mild increase on computation burden, the BODE significantly outperforms the classic NSGA II in terms of solution quality.