Radioactive particle tracking (RPT) technique is a non-invasive velocimetry technique, extensively applied to study hydrodynamics of dense multiphase systems. In this technique, the position of a radioactive tracer particle, designed to mimic the phase of interest, is followed as a Lagrangian marker of point velocity. Computational limitations encountered during tracer particle position reconstruction (which is an inherently slow process) have thus far restricted the use of this versatile technique only to small-scale process vessels. Here, we present a noteworthy improvement over the classical Monte Carlo algorithm for tracer particle position reconstruction, whereby we enhance the convergence and computational speed of the algorithm using Real Coded Genetic Algorithm optimization. This modification results in drastic reduction in computational time required for detector parameter estimation, and altogether eliminates the need for the distance-count map, which was earlier inherent to RPT experimentation. (c) 2017 American Institute of Chemical Engineers AIChE J, 63: 2850-2863, 2017