Even though in some instances optimisation can be computationally expensive, aeronautical industries are now considering with high interest the important cost reduction of introducing optimisation early in the design process. It has also been shown, in various UAVs and UCAVs applications that a multidisciplinary approach can provide additional benefits such as reduction of empty weight, drag and/or radar cross section. One of the new challenges in aeronautics is combining and accounting for multiple disciplines while considering uncertainty or variability in the design parameters or operating conditions. This paper describes a methodology for multidisciplinary design optimisation when there is uncertainty in the operating conditions. The methodology is based on canonical evolution algorithms and incorporates the concepts of multi-objective optimisation, hierarchical (multi-fidelity) topology, asynchronous evaluation and parallel computing (HAPMOEA). This methodology is enhanced by its coupling with an uncertainty analysis technique. The paper illustrates the use of this methodology on three practical test cases with increasing levels of complexity. Stealth aircraft and unmanned aerial vehicles are the ideal candidates due to the multi-physics involved and variability of Sectors to be performed. The first case considers the aerodynamic analysis and optimisation on a UCAV only, the second test compares and illustrates the challenge and benefits on introducing a second discipline (Electro-magnetic) while accounting for uncertainty in the designparameters and operating conditions. Results obtained from the optimisation show that the method is effective to find useful Pareto non-dominated solutions and the future benefit of using Uncertainty design technique.