Aircraft seat ejection systems are essential lifesaving equipment for military pilots. The seat ejections have a success rate of 89-91% in altitudes above 150 m, but ejections at low altitudes (below 150 m) remain a serious concern with a success rate of 51.4%. The success rate of an ejection depends on various parameters such as height for parachute full deployment, ejection posture, spinal alignment, restraints etc. To address this issue a Multi-Objective Optimization (MOO) formulation for contradicting objectives to maximize the height reached by the pilot and to minimize the incidence of spinal injuries to the pilot (quantified by DRI, injury parameter) is setup. Decision making strategies such as, Fuzzy logics and Goal programming methods are discussed to find best points from Pareto fronts according to the nature of emergency. A more comprehensive novel lumped mass model of full spine capable of representing the spine in different posture along the sagittal plane is formulated to address the shortcomings of DRI model. The unavailable data for the model were obtained using inverse parameter identification approach by Eigen frequency matching using Genetic algorithm. A new injury parameter is proposed based on the sum of axial and rotary compressions at each vertebral level, to account for both wedge compression and burst fractures. The MOO approach is carried out based on this novel injury parameter and the dynamic conditions of the aircraft prevailing during the time of emergency to choose the optimal impulse required for safe ejection. A non-linear seat restraint interaction is also modelled to include the effect of varying slackness of seat restraints there by optimizing force applied for the different physical conditions of the pilot, which hasn’t been reported in literature yet. The Pareto solutions obtained thereof are used to train a two stage Artificial Neural Network architecture to choose optimal parameters for any ejection scenario. The ANN proposed was seen to give good results which were along the generated Pareto fronts for different simulated aircraft conditions instantaneously.