One of the principal goals motivating advances in lighting technology is maximization of luminous efficacy, the ratio of the total luminous flux to total power input (i.e., the "amount of light" per Watt). However, colorimetric properties, such as the apparent color and color rendering properties of the light, have a strong influence on the application and adoption of new light source technologies, probably because these properties can easily be directly assessed by consumers. There are cases where the efficacy of a new electric light source technology is very high but the color properties are unsuitable. It is possible to exchange some efficacy for better colorimetric properties: there are an infinite number of ways to filter a broad-spectrum light so that it has better colorimetric properties. However, almost all of these filters will reduce luminous efficacy by an unacceptable amount. A novel approach to multiobjective optimization, the "target objectives genetic algorithm" (TOGA), is introduced, and implemented to determine the tradeoff between luminous efficacy, chromaticity, and color rendering for four filtered lamp spectra. TOGA is a non-Pareto, non-aggregating function approach to multiobjective optimization similar in concept to goal programming. TOGA is computationally very fast, generates multiple optimal points during each run, and can be used for any multiobjective optimization problem. However, TOGA is most efficient when the researcher has some domain knowledge and is able to select good combinations of objectives. In addition, like goal programming, TOGA can result in solutions that are not Pareto-optimal (but are optimal solutions at a particular level of target objectives). The resulting filtered lamp spectra are examined with respect to GA performance, and the deviation from the target chromaticities and target color rendering values are shown to be largely well within lighting industry limits, and the variance in efficiency of the resulting filtered lamp spectra is shown to be low. This technique can be use to determine how new electric light source technologies might be filtered so that they are more commercially viable.