The design of a complex engineering system typically involves tradeoffs among multiple design criteria or disciplinary performance to achieve the optimal design. The design process is usually an iterative procedure with individual discipline sub-systems designed concurrently to meet target values assigned from the system level. One of the most challenging issues is the large number of iterations in this design process, especially when uncertainty is taken into account. To improve the design concurrency while maintaining preferred tradeoffs at the system level, a new method is developed that identifies proper targets based on disciplinary design capability information while optimizing the design goal at the system level. The design capability of a discipline or criterion is represented by the achievable area bounded by its Pareto frontier. Using target values obtained from this method using Pareto information, the number of design iterations can be reduced in both deterministic and probabilistic design scenarios compared to existing approaches, such as Analytical Target Cascading (ATC). To demonstrate applications and benefits of the developed method, this approach is applied to the design of a two-bar truss structure.