As we move away from well-defined problem domains, and get closer to more open-ended domains like planning and design, an increase in the complexity of the problems compel the problem-solving behavior to change in a qualitative sense. Consequently, dynamic problem solving strategies appear as one of the requirements for computational design studies. This paper presents a novel multi-objective Evolutionary Algorithm (EA) called the Interleaved EA (IEA) as a problem-solving tool, which incorporates dynamic aspects. It is specific to IEA that one of the objectives leads the evolution until its fitness progression stagnates. As such, IEA enables the use of different settings and operators for each of its objectives, which would be the same for all objectives in a regular EA. This enables the IEA to dynamically adapt its problem setting throughout its progression. We present the specificities of the IEA with an application on a design problem. As the IEA has been developed to assist in design problems, it is examined through the "Architectural Layout Design" problem studied through library buildings, exemplifying an ill-defined, multi-modal, and multi-objective problem. We compare the functioning of the algorithm with regard to, first, a regular rank-based version, for demonstrating the effect of the leading objective approach; secondly, with a popular multi-objective EA (i.e., NSGA2).We discuss how and why IEA can be used and developed further to incorporate domain specific understanding for multi-modal and dynamic design problems.