This paper presents a multidisciplinary optimization procedure for enhancing the aerodynamic and aeroacoustic performance of a forward-curved blade centrifugal fan for residential ventilation. Flow analysis in a forward-curved blade centrifugal fan was conducted by solving three-dimensional steady and unsteady Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. On the basis of the aerodynamic sources extracted from the unsteady flow, aeroacoustic analysis was implemented in a finite/infinite element method by solving the variational formulation of Lighthill's analogy. Experiments were performed to obtain aerodynamic and aeroacoustic measurements for validation of numerical results. The single-and multi-objective optimizations were performed sequentially. The single-objective optimization was carried out to improve the efficiency of the fan using a radial basis neural network surrogate model with four design variables defining the scroll cutoff angle, scroll diffuser expansion angle, diameter ratio of the impeller, and blade exit angle. Multi-objective optimization based on the single-objective optimization result was carried out to simultaneously improve the efficiency and reduce the sound pressure through a hybrid multi-objective evolutionary algorithm coupled with a response surface approximation surrogate model with two design variables defining the scroll cutoff radius and distance. These objective functions were accessed numerically through threedimensional aerodynamic and aeroacoustic analyses at the design points sampled by Latin hypercube sampling in the design space. Arbitrary selected optimum designs in the Pareto-optimal solutions yielded significant increases in efficiency and decreases in the sound pressure level compared to the reference design.