Optimization techniques are used in inversion of ocean color remote sensing reflectance measurements, where the error between forward modelled spectra and observed spectra is minimized. In this study, NASA Bio – optical Marine Algorithm Dataset (NOMAD) is used to test the performance of global optimization technique based on Multi-Verse Optimization (MVO) for retrieval of Bulk and Individual Inherent optical properties (IOPs) from Remote sensing reflectance (Rrs). The results are compared with other global optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic algorithms (GA) in terms of their statistical goodness of fit and computational time requirements. MVO (743.82 secs) offered computational fastness over both PSO (1261.8 secs) and GA (3818.8 secs). The RMSE values in log space, obtained for bulk IOPs, i.e., total absorption coefficient at 440 nm and total backscattering coefficient at 555 nm using MVO (0.264,0.265), PSO (0.264,0.265) and GA (0.264, 0.274) respectively show that MVO performed either better or similar to PSO and GA. In case of individual IOP retrieval i.e., log scale RMSE values obtained for absorption due to phytoplankton at 440 nm (MVO – 1.038, PSO – 1.200, GA – 1.215), absorption due to gelbstoff at 440 nm (MVO – 0.272, PSO – 0.272, GA – 0.273) and backscattering due to particulate matter at 555 nm (MVO – 0.228, PSO – 0.227, GA – 0.238) showed similar performance as in bulk IOP retrieval. MVO can thus be used effectively on satellite imagery data for retrieval of IOPs owing to its faster computational capability and comparable or better performance to existing global optimization algorithms.