In mod building design, engineers are constantly facing challenging to find an optimal design to maintain a high level of thermal comfort and indoor air quality for occupants while minimizing the system energy consumption. Over the past decades, several algorithms have been proposed and developed for optimizing the heating, ventilation and air conditioning (HVAC) system for indoor environment. Nevertheless, majority of these optimization algorithms are focused on single objective optimization procedures and require large training sample for surrogate modelling. For multi-objective HVAC design problems, previous studies introduced an arbitrary weighting factor to combine all design objectives into one single objective function. The near optimal solutions were however sensitive to the chosen value of the weighting factor. Aiming to develop a multi-objective optimization platform with minimal computational cost, this paper presents a nondominated sorting-based particle swarm optimization (NSPSO) algorithm together with the Kriging method to perform optimization for the HVAC system design of a typical office room. In addition, an adaptive sampling procedure is also proposed to enable the optimization platform to adjust the sampling point and resolution in constructing the training sample. Significant computational cost could be reduced without sacrificing the accuracy of the optimal solution. The proposed methods are applied and assessed in a typical HVAC system and the results indicate that comparing to traditional methods, the presented approach can handle multi-objective optimization in ventilation system with up to 46.6% saving of computational time.