This study addresses a two-stage hybrid flow shop green scheduling problem considering outsourcing, where the manufacturer can outsource the first-stage operations of certain jobs to multiple potential outsourcers, with transportation time and cost considered before the second-stage internal processing. A new mixed-integer linear programming (MILP) model is developed to minimize the weighted sum of makespan, total cost, and total energy consumption. The weights for objectives are determined using the AHP-entropy composite weighting method based on the index number scale. Given the NP-hard nature of the problem, an efficient two-stage heuristic (ETH) algorithm is proposed based on the characteristics of the problem. The ETH algorithm decomposes the problem into two parallel machine scheduling sub-problems while clarifying the priority relationships between stages. In the two stages, the processing sequence of jobs is first determined according to the Johnson’s rule and the first come first serve rule, respectively; followed by allocating jobs to machines based on the greedy idea, with the first stage explicitly considering constraints of transportation time and outsourcing costs. Finally, experimental comparisons demonstrate that the ETH algorithm outperforms the traditional rounding heuristic (RH) algorithmin both solution efficiency and optimization quality.