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Multiobjective building design optimization is a challenging problem because it involves finding a set of solutions that simultaneously optimize multiple conflicting objectives. Simulations-based optimization is widely used, but it is a computationally expensive process in terms of time, as it requires a large number of evaluations of the objective functions. Metamodel-based optimization is an alternative to reduce the time-consuming simulations during the optimization process. Metamodels can approximate the building simulation model with analytical expressions. However, the accuracy of metamodels depends on the number of simulations used to train the model and the sampling strategy used to select informative samples over the design space. This study proposes an efficient sequential sampling approach to fit the metamodels toward the regions of the design space where their accuracy is higher and can improve all objectives simultaneously. To demonstrate the effectiveness of this approach, it was applied to optimize the energy and investment costs of a multi-story residential building. The optimization results were compared with those obtained using a non-dominated sorted genetic algorithm II (NSGA-II). The results of this study show that the proposed method reduces the number of building energy simulations required by up to 50% while guaranteeing accurate optimization results. Fifteen energy-efficient buildings designs were proposed, with a wide range of trade-offs between energy and investment costs. This study highlights the potential of the proposed approach to achieve faster and accurate building design optimization and allowing for a larger design space, leading to more creative and innovative solutions.