KEYWORDS Building energy management system; Multi-objective antlion optimizer; Demand side scheduling; Multi-criteria decision making; CO2 emission; Evidential reasoning.Abstract. Smart-Home Energy Management Systems (SHEMSs) are widely used for energy management in smart buildings. Energy management in smart homes is an arduous task and necessitates e cient scheduling of appliances in buildings. Scheduling of smart appliances is usually enmeshed by various and sometimes contradictory criteria, which should be considered concurrently in the scheduling process. Multi-Criteria Decision Making (MCDM) techniques are able to select the most suitable alternative among copious ones. This paper tailors a comprehensive framework which merges MCDM techniques with Evolutionary Multi-Objective Optimization (EMOO) techniques for selecting the most proper schedule for appliances by creating a trade-o between optimization criteria. A Multi-Objective Ant Lion Optimizer (MOALO) was tailored and tested on a smart home case study to detect all the Pareto solutions. A benchmark instance of the appliance scheduling was solved employing the proposed methodology. Then, Shannon's entropy technique was employed to nd the weights corresponding to the objectives. Finally, the acquired Pareto optimal solutions were ranked utilizing the Evidential Reasoning (ER) method. By inspecting the e ciency of every solution considering multiple criteria such as unsafety, electricity cost, delay, Peak to Average Ratio (PAR), and CO 2 emission, e ectiveness of the proposed approach in enhancing the method for smart appliance scheduling was con rmed.ownership [1]. Therefore, improvement in the energy e ciency of electrical facilities is very in uential for energy-saving in buildings, reducing the loads on electrical grids, and decreasing the carbon footprint. Consequently, electricity conservation in buildings not only results in saving fossil fuels but also prevents capacity expansion in the power sector [2,3]. Many research results are available for supporting the decisions in the management of networks [4,5]. The emergence of smart homes and the Internet has led to an opportunity for automatic operation, scheduling of the appliances, and energy management in residential buildings.