Universal generating function ( UGF) method has great advantages in dealing with probabilistic analysis problems with non-normal random variables and highly non-linear performance functions. A meth¬od of sequential optimization and reliability assessment based on universal generating function (UGF-SO¬RA )is proposed to improve the optimization accuracy of structural reliability. In the porposed method, the high precision reliability analysis method and the offset vector solving strategy are introduced for opti¬mization, and the iteration process is divided into three parts. In the first step, the least square method is used to fit the response surface regression model of migration function, and the offset vectors are solved according to the model and the permissible reliability index. In the second step, according to the offset vector, the deterministic optimization is completed and the current design points are obtained. In the third step, the reliability analysis and evaluation are carried out by UGF method. Furthermore, the miUniversal generating function ( UGF) method has great advantages in dealing with probabilistic analysis problems with non-normal random variables and highly non-linear performance functions. A meth¬od of sequential optimization and reliability assessment based on universal generating function (UGF-SO¬RA )is proposed to improve the optimization accuracy of structural reliability. In the porposed method, the high precision reliability analysis method and the offset vector solving strategy are introduced for opti¬mization, and the iteration process is divided into three parts. In the first step, the least square method is used to fit the response surface regression model of migration function, and the offset vectors are solved according to the model and the permissible reliability index. In the second step, according to the offset vector, the deterministic optimization is completed and the current design points are obtained. In the third step, the reliability analysis and evaluation are carried out by UGF method. Furthermore, the migration function is reconstructed according to the relevant constraints. It is shown through the example a¬nalysis that the proposed method is used to ensure the efficiency of solution, but also improve the accura¬cy of optimization. It also solves the problem that the optimization results can^ converge when the per¬formance function is highly non-linear. [ABSTRACT FROM AUTHOR]