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This paper focuses on solving a problem of green location-routing with cold chain logistics (GLRPCCL). Considering the sustainable effects of the economy, environment, society, and cargos, we try to establish a multi-objective model to minimize the total cost, the full set of greenhouse gas (GHG) emissions, the average waiting time, and the total quality degradation. Several practical demands were considered: heterogeneous fleet (HF), time windows (TW), simultaneous pickup and delivery (SPD), and a feature of mixed transportation. To search the optimal Pareto front of such a nondeterministic polynomial hard problem, we proposed an optimization framework that combines three multi-objective evolutionary algorithms (MOEAs) and also developed two search mechanisms for a large composite neighborhood described by 16 operators. Extensive analysis was conducted to empirically assess the impacts of several problem parameters (i.e., distribution strategy, fleet composition, and depots’ time windows and costs) on Pareto solutions in terms of the performance indicators. Based on the experimental results, this provides several managerial insights for the sustainale logistics companies.