This paper presents a comprehensive approach to the design optimisation of canard planforms for a typical fighter aircraft. Primary objective of optimisation is to enhance aircraft’s aerodynamic efficiency while honouring other critical performance metrics. Design constraints are imposed to ensure feasibility and practicality. The optimisation framework integrates a robust geometry generation module, an aerodynamic analysis module and a genetic algorithm-based optimisation process utilizing a surrogate model. Geometry generation module is a catalogue-based component generator offering significant design flexibility. Aerodynamics module consists of an automated grid generator and a robust Euler CFD solver. The grid generator is constructive solid geometry based and is capable of handling a wide range of geometric applications. A surrogate model is synthesized based on aerodynamic analysis data to facilitate proper domain exploration and faster execution for an optimal canard configuration. Individual modules are validated, ensuring that geometric and aerodynamic behaviour of a typical fighter aircraft are efficiently captured. The optimisation results demonstrate effectiveness of the framework in identifying several optimal canard configurations meeting or exceeding performance targets. These configurations are further scrutinized through detailed high-fidelity RANS simulations to ensure robustness and reliability. The proposed methodology offers a robust and efficient toolset for iterative design refinement of fighter aircraft canards, with potential applications to other aerodynamic surfaces.