The extensive application requirements of lead-bismuth reactors require researchers to carry out a lot of optimization design work on the basis of existing core schemes. Aiming at the multi-dimensional nonlinear constrained optimization design problem of lead-bismuth reactor with multi-physical, multi-variable and multi-constraint coupling effects, an intelligent optimization method for lead-bismuth reactor was constructed based on Kriging surrogate model, orthogonal Latin hypercube sampling and SEUMRE spatial search technology. Coupled with physical Monte Carlo calculation/thermal ranalysis code, an optimization platform including sampling, pre-and post-processing of coupling program and reactor optimization analysis function was developed. Taking SPALLER-4 and URANUS as prototypes, the scheme optimization and parameter optimization verification of minimum fuel load were carried out respectively. The verification results show that the core intelligent optimization method is feasible and effective for the optimization of lead-bismuth reactor design scheme and core parameters. Compared with the traditional Monte Carlo calculation optimization, the calculation cost is greatly reduced under the premise of ensuring the prediction accuracy. Compared with the URANUS initial model, the fuel loading, the total mass of the core, the volume of the active zone and the total volume of the core are optimized by 10.8%, 11.5%, 18.1% and 17.1% respectively, which provides a reference for the intelligent optimization method based on the surrogate model applied to the optimization design of lead-bismuth reactor.