Effective management of warehouse operations is critical not only for reducing response time and inbound costs, but also for its potential impact on energy consumption and CO2 emissions. This case study fronts location allocation problem is fronted using an evolutionary Genetic algorithm to explore the mutual impact of travel time, total inbound costs and CO2 emissions. The research is developed within the finished products warehouse of an automotive industry, analyzing the operations for the whole warehouse cycle, including the arrival of dirty containers/component-boxes, storage, cleaning, re-storage and delivery of cleaned containers/boxes. The solutions are explored by solving the integer programming model developed for travel time, inbound costs and CO2 emissions. Then, within the location allocation problem the study is augmented with the priorities’ options for the containers, to explore the three different perspectives of optimizing travel time, inbound costs or CO2 emission. Results highlight the possibilities of concurrent improvement for these objectives with significant decreasing of all three parameters, i.e. time, cost and CO2 emissions. There is a reduction in costs of 54.43%, a reduction in time of 79.49% and a reduction in CO2 emissions of 54.16%. A sensitivity analysis is further carried out to assess how the variation of these parameters impacts on the optimal solution.