Estimating the source parameters of gravitational waves from compact binary coalescence (CBC) is a key analysis task in gravitational-wave astronomy. To deal with the increasing detection rate of CBC signals, optimizing the parameter estimation analysis is crucial. The analysis typically employs a stochastic sampling technique such as Markov Chain Monte Carlo (MCMC), where the source parameter space is explored and regions of high-Bayesian posterior probability density are found. One of the bottlenecks slowing down the analysis is the nontrivial correlation between masses and spins of colliding objects, which makes the exploration of mass-spin space extremely inefficient. We introduce a new set of mass-spin sampling parameters which makes the posterior distribution simpler in the new parameter space, regardless of the true values of the parameters. The new parameter combinations are obtained as the principal components of the Fisher matrix for the restricted 1.5 post-Newtonian waveform. Our reparametrization improves the efficiency of MCMC by a factor of ∼10 for a binary neutron star with a narrow-spin prior (|χ →|<0.05 ) and ∼100 for a binary neutron star with a broad-spin prior (|χ →|<0.99 ), under the assumption that the binary has spins aligned with its orbital angular momentum.