Aero-engine assembly is the core tache in the whole process of aero-engine manufacturing. Assembly variations are unavoidable due to parts’ geometrical errors. Statistical variation analysis is an effective method for robust design that can quantitatively predict product quality in the original design stage. However, traditional methods focus on the modeling of plane dimension chain and extremum analysis, which is difficult to comprehensively consider the rich geometrical errors and their relationship to each other; meanwhile, the precision prediction is too conservative to reduce the parts’ rework frequency and adjusting difficulty; in addition, traditional methods overemphasize the promotion of parts’ machining precision, and ignore the means of overall stack optimization. To overcome these problems, firstly, Jacobian-Torsor (J-T) model is used to build the variation propagation, which is well suited to a complex assembly that contains large numbers of joints and geometric tolerances; secondly, combining with Monte Carlo simulation and J-T statistical contribution solution, the percentage contribution of each part could be solved; Finally, Taguchi multi-objective optimization method is adopted for robust design of the whole system. A case study on an entire aero-engine assembly is presented to illustrate the proposed method and the results show that this new method can effectively evaluate the assembly performance and determine the optimal assembly plan, which has strong practical guiding significance.