The increasing demand for lightweight and cost-effective structures in aerospace and wind energy applications has led to widespread adoption of fibre-reinforced polymers (FRPs). While carbon fibre reinforced polymers (CFRPs) offer excellent stiffness-to-weight ratios, their high cost and poor damage tolerance pose challenges for structural applications such as wind turbine and helicopter rotor blades. Hybrid laminates combining CFRPs and low-cost, higher failure strain (compared to CFRP) Glass fibre reinforced polymers (GFRPs) present a promising alternative, balancing competing objectives of cost and stiffness while providing higher damage tolerance. Most existing optimisation studies of hybrid composite plates treat material selection and ply orientation as decoupled problems, lacking a unified formulation that can capture their interaction holistically. Furthermore, existing approaches prevent the application of gradient-based optimisation via lamination parameters(LP), efficient for stiffness-targeted optimisation, when attempting to unify material assignment and ply orientation within a single design framework. This thesis develops a novel method to enable the use of lamination parameters for hybrid composite laminates by introducing material-dependent dispersion parameters(DP), unifying material selection and ply orientation within a single design formulation. With the material properties of the laminate as a function of volume fraction, the dispersion parameters describe the through-thickness material distribution in a continuous, geometry-independent manner. The combined LP–DP formulation defines a continuous, convex design region amenable to gradient-based optimisation frameworks. The DPs and feasible region are further explored to create a combinatorics-based and deterministic method to constrain the region, creating a relation between LPs and DPs. Further, different optimisation strategies have been discussed to achieve an optimum solution for design problems. A two-level optimisation strategy is roposed, combining a gradient-based optimisation for target parameter identification and a genetic algorithm to retrieve manufacturable, symmetric, and balanced stacking sequences that comply with standard layup rules.