The development of power generation technologies and the deregulation of the power market has led to an increasing interest in distributed power generation, mainly the simultaneous exploitation of electricity and heat from the same energy source, known as combined heat and power (CHP) systems. As a consequence of the high competitiveness of power markets and increasing environmental concerns, distributed power generators have to make reasonable choices at multiple levels of complexity. A key issue to successfully approaching these problems is the development of decision making support tools that rely on service life prediction, intelligent economic dispatch optimisation techniques and condition monitoring. This research introduces the concept and development of a decision making support tool for a mini-pool nerve centre based on distributed gas turbine generation units operating in a competitive market. The nerve centre framework leads naturally to a multi-criteria optimisation problem which is solved in this research with a hybrid genetic algorithm adapted priority list and creep life assessment. The proposed hybrid approach can result in a significant saving to generators as it efficiently optimises mini- pool profits and service hours between failures in an acceptable computation time and accurately. Life cycle assessment combined with generation schedule optimisation can enhance the maintenance strategy activities and the competitiveness of gas turbine distributed generation plants, particularly for generators trading energy in a highly competitive market. Gas turbine combined heat and power distributed generators are unlikely to succeed in competing individually with centralised generation technologies within the present market framework, but they can be more competitive in an information technology based mini-pool. Additionally, results show that the development of a low carbon emission power industry can result in an outstanding opportunity for combined heat and power mainly in power markets currently highly dependent on coal and oil powered stations.