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.