Computational intelligence based modeling and optimization techniques are employed primarily to investigate the role of the composition and processing parameters on the mechanical properties of API grade microalloyed pipeline steel and then to design steel having improved performance in respect to its strength, impact toughness and ductility. Artificial Neural Network (ANN) models, capable of prediction and diagnosis in non-linear and complex systems, are used to obtain the relationship of composition and processing parameters with said mechanical properties. Then the models are used as objective functions for the multi-objective genetic algorithms for evolving the tradeoffs between the conflicting objectives of achieving improved strength, ductility and impact toughness. The Pareto optimal solutions are analyzed successfully to study the role of various parameters for designing pipeline steel with such improved performance.