Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and / or maximized concurrently. Many cross-objective optimization algorithms have been developed; Many few of them are tested in solving building design problems.This paper compared performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building (n ZEB) where more than 1.610 solutions would be possible. The comparable algorithms include a controlled non-dominated sorting genetic algorithm witha a two-phase optimization using the genetic algorithm (PR_GA), an elitist non-dominated sorting evolution strategy (ENSES), a multi-objective particle swarm optimization (MOPSO) algorithm based on the concept of epsilon dominance (ev MOGA), a multi-objective differential evolution algorithm (sp MODE-II), and a multi-objective drag onfly algorithm (MODA) .Several criteria was used to compare performance of these algorithms.In most cases, the quality of the obtained solutions was improved when the number of generations was increased. The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of ​​the solution-space and achieved close-to-optimal solutions with a good diversity, followed by the p NSGA-II, ev MOGA and sp MODE-II. Uncompetitive results were achieved by the ENSES, MOPSO and MODA in most running cases. The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model.