Clonal Selection Algorithm (CLONALG) is one of the many branches of the Artificial Immune System Algorithms that are used to tackle engineering problems . Inspired by the adaptive immune response of living organisms, CLONALG combines the clonal selection principle with the affinity maturation process, where those antibodies that successfully recognize the invading antigens are selected to proliferate . Through evolution-based maturation of high affinity antibodies, the algorithm progresses towards finding a global solution to the optimization problem at hand. CLONALG has been shown to be very effective on multi-pole problems due to its inherent properties, yet it has not become a popular choice among optimization tools in the electromagnetics community. The primary reason for this is the large number of clones generated at each iteration, which increases the computational burden when used in conjunction with a full-wave electromagnetic solver. In this paper, we demonstrate that a successful parallel implementation of CLONALG would decrease the computational burden of multiple calls to the electromagnetic solver by distributing the work load over multiple processors. The optimization of a multi-layered frequency selective surface (FSS) filter for the X-band will be presented and compared with the more traditional parallel genetic algorithm (GA) optimizer in order to demonstrate the effectiveness of the parallel CLONALG implementation in the area of electromagnetics design.