Clinical OSSN diagnostics by non-invasive spectral imaging of eye autofluorescence must be rapid enough to be comfortable for patients - without compromising accuracy. This requires identifying optimized spectral signatures of OSSN based on a minimal number of spectrally defined images. Here, we identified such signatures using a data-driven methodology of swarm intelligence. Ten patients with histopathological diagnosis of ocular surface squamous neoplasia (OSSN) were recruited. Their unstained biopsy OSSN specimens were investigated using a custom-built autofluorescence multispectral microscopy imaging system. The images were taken in 38 spectral channels spanning specific excitation (340 nm-510 nm) and emission (420 nm-650 nm) wavelength ranges. To identify optimized spectral signatures of OSSN from a small number of channels, swarm intelligence was combined with discriminative cluster analysis. This study established an optimized spectral signature of OSSN derived from multispectral data taken in 38 channels. Depending on the critical nature of the application and the consequences of misclassification error, two optimized spectral signatures with 5 and 10 channels were obtained which reduced the imaging time to 20 and 40 seconds, a reduction by 75% and 80 %, respectively. The K-nearest neighbor classifier was then built using OSSN spectral signatures and optimized to successfully detect OSSN with 1% and 14% misclassification error using 10 and 5 channels, respectively. Our study found an optimized spectral signature of OSSN allowing rapid diagnostic imaging in clinical settings and demonstrates the feasibility of using optimized multispectral autofluorescence spectral signatures to detect and determine boundaries of OSSN.