The industrial sector is one of the world’s largest energy consumers. Moreover, the fluctuating energy costs make effective energy management a critical challenge for the manufacturing industry. To address these challenges, companies are increasingly focusing on optimizing energy-efficient scheduling practices. In the current work, we addressed the two-stage no-wait hybrid flowshop problem, aiming to minimize the total completion time and energy consumption. We initially introduced a mixed integer linear programming (MILP) model when the augmented epsilon-constraint is employed to generate the optimal Pareto front for small instances. In the second step, an efficient bi-objective iterated local search algorithm is introduced to solve a benchmark of 100 instances. The results obtained are compared against those from the Non-dominated Sorting Genetic Algorithm. The comparative analysis demonstrates our proposal’s superior effectiveness.