Existing computational methods for strain optimization generate engineering strategies only one at a time, out of the myriads of possibilities, thus limiting the array of choices presented to the biotechnologist. In addition, metabolic flux data (e.g., though MFA) obtained for the wild-type strain are not directly integrated with the strain optimization process. To remedy these limitations, we present a new computational framework that overcomes these issues. The key concept here is that instead of looking for specific engineering strategies one at a time, we look to classify all fluxes in the metabolic model depending upon whether or not they must increase, decrease, or become equal to zero to meet a pre-specified overproduction target. This classification is not limited to individual reactions but is extended for pairs, triplets, etc. while making use of all flux data available for the strain before engineering. The final output of this analysis is a logic tree-diagram that spans all possible sets of engineering interventions capable of meeting a pre-specified overproduction target. The developed methodology is tested by exhaustively identifying all engineering interventions for succinate production in a glucose-limited medium. The method recapitulates known engineering targets but also reveals new non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis.