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We aim to determine the pipeline operation configurations requiring the minimum amount of energy (e.g. fuel, power) needed to operate the equipment at compressor stations for given transportation requirements. Considering the problem as a search for the optimal operation conditions, speedup of the process can be achieved by careful formulation of the search model and integration of knowledge about the application into the search control. Knowledge incorporation will not only be used to reduce the solution space and guide the search process but also to create heuristics that characterize a proper initial state for every search. Due to the complexity of the fuel cost function (non-convex, non-smooth) selection and proper design of the search paradigm to solve the optimization problem is crucial. Genetic algorithms have been chosen as initial optimization technique to address the optimization problem. Each candidate solution generated by the search algorithm will be evaluated by a hydraulic model that simulates the steady state gas flow in the pipeline network to obtain the reaction of the system at specific control nodes and determine the feasibility of the given solution and perhaps evaluate if it is near-to-optimal. The new method will assist in the optimization of pipeline operations by providing a portfolio of feasible near-to-optimum solutions to decision makers in a timely manner. This set of solutions will represent the network operation conditions that decrease energy consumption. Reduction of energy use will not only have a tremendous economical impact but, nevertheless an environmental one: the more efficient the use of compressors stations is the less greenhouse emissions are dissipated.