To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of various metallic fillers in particulate reinforced flexible mould material composites, multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Here we have adopted two EA-based algorithms namely NSGAII and SPEA2 in order to solve the present problem independently. Comparative study of the results reveals that NSGAII performs better over SPEA2 for investigating the role of metallic fillers in particulate reinforced flexible mould material composites. A recently proposed innovization procedure is also used to unveil salient properties associated with the obtained trade-off solutions. These solutions are analyzed to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mould material. Based on the findings through investigations, the optimal selection of materials is suggested including the cost implication factor.