In engineering design, the performance of system and the budget to control the uncertainty of design should be balanced, which means that it is better to select the design variable and allocate the size of uncertainty simultaneously. This work formulates this problem as an uncertainty optimization problem, where the input uncertainty is modeled by probability method and both the design variables and the uncertainty magnitude are included on the optimization variables. A sequence optimization framework is proposed to solve the optimization problem. Taylor-based first order method is used to translate the probability constraint into a deterministic constraint. A correction coefficient is calculated by dimensional adaptive polynomial chaos expansion method to improve the accuracy of the uncertainty analysis. The constraint translation and the correction coefficient calculation are executed sequentially. The accuracy and effectiveness of the proposed framework are validated by three benchmark problems, including a mathematical problem, a cantilever I-beam and a ten-bar truss case.